As a Custom Software Engineer, you will engage in the development of custom software solutions that involve designing, coding, and enhancing various components across systems or applications. Your typical day will include collaborating with team members to implement modern frameworks and agile practices, ensuring the delivery of scalable and high-performing solutions that are tailored to meet specific business needs. You will also participate in discussions to address challenges and contribute innovative ideas to improve the software development process. Roles & Responsibilities: - Proven ability to work with Java SpringBoot- JavaScript knowledge is nice to have- Understanding of Event based communication with Apache Kafka- Experience with test automation and continuous integration & continuous delivery (Github, Jenkins, Azure, Test & Lint, etc.)- Understanding of AI/ML specifically GenAl and LLMs would be favorable.- Design / UX understanding and sensibility is a nice to have.- Expert knowledge in Kafka is a nice to have.- Expected to perform independently and become an SME.- Required active participation/contribution in team discussions.- Contribute in providing solutions to work related problems.- Assist in the design and architecture of software components to ensure they meet business requirements.- Collaborate with cross-functional teams to gather and analyze requirements for software development. Professional & Technical Skills: - Must To Have Skills: Proficiency in Spring Boot.- Good To Have Skills: Experience with JavaScript, Automated Testing, Java Enterprise Edition.- Strong understanding of RESTful API design and development.- Experience with microservices architecture and cloud deployment.- Familiarity with database technologies such as SQL and NoSQL. Additional Information: - The candidate should have minimum 3 years of experience in Spring Boot.- This position is based at our Bengaluru office.- A 15 years full time education is required.
Responsibilities
As a Custom Software Engineer, you will engage in the development of custom software solutions that involve designing, coding, and enhancing various components across systems or applications. Your typical day will include collaborating with team members to implement modern frameworks and agile practices, ensuring the delivery of scalable and high-performing solutions that are tailored to meet specific business needs. You will also participate in discussions to address challenges and contribute innovative ideas to improve the software development process. Roles & Responsibilities: - Proven ability to work with Java SpringBoot- JavaScript knowledge is nice to have- Understanding of Event based communication with Apache Kafka- Experience with test automation and continuous integration & continuous delivery (Github, Jenkins, Azure, Test & Lint, etc.)- Understanding of AI/ML specifically GenAl and LLMs would be favorable.- Design / UX understanding and sensibility is a nice to have.- Expert knowledge in Kafka is a nice to have.- Expected to perform independently and become an SME.- Required active participation/contribution in team discussions.- Contribute in providing solutions to work related problems.- Assist in the design and architecture of software components to ensure they meet business requirements.- Collaborate with cross-functional teams to gather and analyze requirements for software development. Professional & Technical Skills: - Must To Have Skills: Proficiency in Spring Boot.- Good To Have Skills: Experience with JavaScript, Automated Testing, Java Enterprise Edition.- Strong understanding of RESTful API design and development.- Experience with microservices architecture and cloud deployment.- Familiarity with database technologies such as SQL and NoSQL. Additional Information: - The candidate should have minimum 3 years of experience in Spring Boot.- This position is based at our Bengaluru office.- A 15 years full time education is required.
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
These skills are essential because the applications now exist as Ab Initio graphs rather than COBOL
programs.
Proficiency in the Ab Initio Graphical Development Environment (GDE): Building, modifying,
and debugging graphs using standard components (Reformat, Join, Sort, Rollup, Normalize,
Lookup, etc.), custom transforms, and embedded code.
Understanding and Assessing Auto-Converted Graphs: Graphs produced by Ab Initio's
automated COBOL/IMS conversion tool are not standard hand-built graphs. They follow
machine-generated patterns — often verbose, deeply nested, or structured in ways that differ
significantly from graphs written from scratch. In this environment, these converted graphs must
be assessed and modified to implement new requirements or fix defects. This requires the
ability to trace generated logic back to the original COBOL source, identify the relevant transform
or component within an auto-generated structure, and make targeted, safe changes without
disrupting the surrounding converted logic.
Metadata Management: Working with the Enterprise Meta Environment (EME) for version
control, dependency analysis, impact analysis, and data lineage.
Parameter Handling: Using Parameter Definition Language (PDL) effectively.
Orchestration and Workflow: Conduct It (or Express It) for scheduling and managing job flows
— this largely replaces JCL and IMS transaction management. Job scheduling is handled via
Atomic Automation, which orchestrates Ab Initio workloads in the production environment. A
critical aspect of this environment is that Atomic Automation workflows contain parallel job
dependencies — multiple jobs may execute concurrently with interdependencies that must be
understood when diagnosing failures or assessing the impact of a change. This is distinct from the
sequential step-by-step flow within an individual job; the broader workflow topology must also be
considered.
Data Flow Traceability and File/Dataset Lineage: A critical problem-solving skill in this
environment is the ability to trace data content backwards and forwards through job flows
and workflows — following a file or dataset from its point of creation through each transformation
it undergoes across jobs, graphs, and workflow stages. This includes understanding what
populates a file, how it is transformed at each step, where it is consumed downstream, and how
parallel workflow paths may contribute to or depend on its content. This traceability underpins
three core data concerns that must always be considered:
Data Integrity: Ensuring that transformations preserve the accuracy and consistency of data
values as they move through the system — detecting where values may be incorrectly
computed, overwritten, or corrupted relative to what the original IMS application would have
produced.
Missing Data: Identifying conditions under which records or fields may be absent, dropped,
or skipped — whether due to filtering logic, join mismatches, conditional branches, or
upstream job failures — and understanding the downstream impact of that absence.
Data Retention: Understanding how long data persists at each stage — which files are
transient (used within a single run), which are retained across cycles, and how GDG-style
generational patterns control the lifecycle of datasets. Knowing what data is available, for
how long, and under what conditions is essential for recovery, reprocessing, and audit
support.
Data Processing and Integration: Handling large-scale ETL/ELT processes, including migrated
IMS segment data, copybooks, EBCDIC, packed decimal, and zoned decimal formats.
Administration and Operations: Co Operating System runtime management, monitoring,
logging, error handling, deployment, and performance tuning (parallelism, multifile systems,
resource optimization).
Testing, Validation, and Move to Production (MTP): This is a multi-layered discipline in the
converted Ab Initio environment and must be treated as a structured process, not an afterthought.
Unit Testing of Converted Graphs: Changes to auto-converted graphs require targeted unit
testing at the graph or component level — isolating the modified logic, constructing or
sourcing representative input data, and verifying that outputs match expected results relative
to the original IMS behavior. Because the converted code was machine-generated, even
small changes can have non-obvious ripple effects within the surrounding graph structure;
unit testing must be thorough and deliberate.
Data-Driven Validation: Test cases must be grounded in real or representative data —
including edge cases common in the original IMS environment (e.g., packed decimal
boundary values, missing segments, GDG rollover conditions). Comparing Ab Initio output
against known-good baseline results from the original system (or a prior run) is the most
reliable validation approach.
End-to-End and Integration Testing: Because jobs within workflows have parallel
dependencies, changes must be tested not just at the graph level but across the full job flow
— verifying that upstream outputs feed correctly into downstream jobs and that no parallel
branches are disrupted.
Move to Production (MTP) Coordination: MTP in this environment requires understanding
and coordinating multiple interdependent activities: packaging and promoting Ab Initio graph
changes through the EME; updating or validating Atomic Automation workflow definitions if
job dependencies change; confirming that MFS screen-related graph changes are consistent
with the deployed screen definitions; communicating the scope and timing of changes to
operations and business stakeholders; and verifying that production data files and GDG
generations are in the correct state prior to cutover. A practitioner must also understand the
rollback implications of a failed MTP — what state files and workflows will be in, and what
steps are needed to recover."
Responsibilities
These skills are essential because the applications now exist as Ab Initio graphs rather than COBOL
programs.
Proficiency in the Ab Initio Graphical Development Environment (GDE): Building, modifying,
and debugging graphs using standard components (Reformat, Join, Sort, Rollup, Normalize,
Lookup, etc.), custom transforms, and embedded code.
Understanding and Assessing Auto-Converted Graphs: Graphs produced by Ab Initio's
automated COBOL/IMS conversion tool are not standard hand-built graphs. They follow
machine-generated patterns — often verbose, deeply nested, or structured in ways that differ
significantly from graphs written from scratch. In this environment, these converted graphs must
be assessed and modified to implement new requirements or fix defects. This requires the
ability to trace generated logic back to the original COBOL source, identify the relevant transform
or component within an auto-generated structure, and make targeted, safe changes without
disrupting the surrounding converted logic.
Metadata Management: Working with the Enterprise Meta Environment (EME) for version
control, dependency analysis, impact analysis, and data lineage.
Parameter Handling: Using Parameter Definition Language (PDL) effectively.
Orchestration and Workflow: Conduct It (or Express It) for scheduling and managing job flows
— this largely replaces JCL and IMS transaction management. Job scheduling is handled via
Atomic Automation, which orchestrates Ab Initio workloads in the production environment. A
critical aspect of this environment is that Atomic Automation workflows contain parallel job
dependencies — multiple jobs may execute concurrently with interdependencies that must be
understood when diagnosing failures or assessing the impact of a change. This is distinct from the
sequential step-by-step flow within an individual job; the broader workflow topology must also be
considered.
Data Flow Traceability and File/Dataset Lineage: A critical problem-solving skill in this
environment is the ability to trace data content backwards and forwards through job flows
and workflows — following a file or dataset from its point of creation through each transformation
it undergoes across jobs, graphs, and workflow stages. This includes understanding what
populates a file, how it is transformed at each step, where it is consumed downstream, and how
parallel workflow paths may contribute to or depend on its content. This traceability underpins
three core data concerns that must always be considered:
Data Integrity: Ensuring that transformations preserve the accuracy and consistency of data
values as they move through the system — detecting where values may be incorrectly
computed, overwritten, or corrupted relative to what the original IMS application would have
produced.
Missing Data: Identifying conditions under which records or fields may be absent, dropped,
or skipped — whether due to filtering logic, join mismatches, conditional branches, or
upstream job failures — and understanding the downstream impact of that absence.
Data Retention: Understanding how long data persists at each stage — which files are
transient (used within a single run), which are retained across cycles, and how GDG-style
generational patterns control the lifecycle of datasets. Knowing what data is available, for
how long, and under what conditions is essential for recovery, reprocessing, and audit
support.
Data Processing and Integration: Handling large-scale ETL/ELT processes, including migrated
IMS segment data, copybooks, EBCDIC, packed decimal, and zoned decimal formats.
Administration and Operations: Co Operating System runtime management, monitoring,
logging, error handling, deployment, and performance tuning (parallelism, multifile systems,
resource optimization).
Testing, Validation, and Move to Production (MTP): This is a multi-layered discipline in the
converted Ab Initio environment and must be treated as a structured process, not an afterthought.
Unit Testing of Converted Graphs: Changes to auto-converted graphs require targeted unit
testing at the graph or component level — isolating the modified logic, constructing or
sourcing representative input data, and verifying that outputs match expected results relative
to the original IMS behavior. Because the converted code was machine-generated, even
small changes can have non-obvious ripple effects within the surrounding graph structure;
unit testing must be thorough and deliberate.
Data-Driven Validation: Test cases must be grounded in real or representative data —
including edge cases common in the original IMS environment (e.g., packed decimal
boundary values, missing segments, GDG rollover conditions). Comparing Ab Initio output
against known-good baseline results from the original system (or a prior run) is the most
reliable validation approach.
End-to-End and Integration Testing: Because jobs within workflows have parallel
dependencies, changes must be tested not just at the graph level but across the full job flow
— verifying that upstream outputs feed correctly into downstream jobs and that no parallel
branches are disrupted.
Move to Production (MTP) Coordination: MTP in this environment requires understanding
and coordinating multiple interdependent activities: packaging and promoting Ab Initio graph
changes through the EME; updating or validating Atomic Automation workflow definitions if
job dependencies change; confirming that MFS screen-related graph changes are consistent
with the deployed screen definitions; communicating the scope and timing of changes to
operations and business stakeholders; and verifying that production data files and GDG
generations are in the correct state prior to cutover. A practitioner must also understand the
rollback implications of a failed MTP — what state files and workflows will be in, and what
steps are needed to recover."
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
Training & Culture Building
• Conduct workshops, labs, AI coaching sessions for engineers & managers.
• Lead internal communities of practice for AI and GenAI.
• Promote innovation through showcases, hackathons, and associated central initiatives.
Metrics & Continuous Improvement
• Report & Establish KPIs: productivity gains, adoption rates, automation impact (aligned with THRIVE).
Required Skills & Experience
Technical Skills
• Strong understanding of GenAI, LLMs, vector databases, ML workflows. (cont learning)
• Experience integrating AI into development workflows (copilots, test automation, documentation).
• Proficiency in Python/Java and cloud platforms (Azure/AWS/Google).
• Good grasp of enterprise SDLC, DevOps, APIs, microservices, security, and compliance.
Influence & Leadership
• Proven ability to influence teams without formal authority.
• Excellent stakeholder management across verticals and global counterparts.
• Translate complex AI topics into simple, actionable guidance.
• Align with central initiatives to drive AI adoption in respective dept.
Mindset & Traits
• Evangelist mindset, proactive learner, strong communicator.
• Comfortable with ambiguity and fast experimentation.
• Collaborative, customer-centric, and outcome-driven.
Preferred Qualifications
• 9+ years in software engineering, architect, delivery, or enterprise architecture.
• Experience in transformation programs / self-driver of owned initiatives.
• Exposure to enterprise-scale systems.
• Certifications in AI/ML, cloud, or agile practices.
Responsibilities
Training & Culture Building
• Conduct workshops, labs, AI coaching sessions for engineers & managers.
• Lead internal communities of practice for AI and GenAI.
• Promote innovation through showcases, hackathons, and associated central initiatives.
Metrics & Continuous Improvement
• Report & Establish KPIs: productivity gains, adoption rates, automation impact (aligned with THRIVE).
Required Skills & Experience
Technical Skills
• Strong understanding of GenAI, LLMs, vector databases, ML workflows. (cont learning)
• Experience integrating AI into development workflows (copilots, test automation, documentation).
• Proficiency in Python/Java and cloud platforms (Azure/AWS/Google).
• Good grasp of enterprise SDLC, DevOps, APIs, microservices, security, and compliance.
Influence & Leadership
• Proven ability to influence teams without formal authority.
• Excellent stakeholder management across verticals and global counterparts.
• Translate complex AI topics into simple, actionable guidance.
• Align with central initiatives to drive AI adoption in respective dept.
Mindset & Traits
• Evangelist mindset, proactive learner, strong communicator.
• Comfortable with ambiguity and fast experimentation.
• Collaborative, customer-centric, and outcome-driven.
Preferred Qualifications
• 9+ years in software engineering, architect, delivery, or enterprise architecture.
• Experience in transformation programs / self-driver of owned initiatives.
• Exposure to enterprise-scale systems.
• Certifications in AI/ML, cloud, or agile practices.
Salary : Rs. 15,00,000.0 - Rs. 20,00,000.0
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
Lead Software Engineer - Test Automation - (25000JDZ)
Missions
Key Responsibilities
Lead and support change management activities across the product change and release management process
Define, implement, and own test strategies, test plans, and quality metrics
Hands-on involvement in manual and automation testing, with a focus on risk-based testing
Design, review, and maintain test cases, test scenarios, and automation frameworks
Guide and mentor QA team members on best practices, tools, and frameworks
Review test results, identify defects, perform root cause analysis, and drive defect resolution
Collaborate closely with developers, product owners, DevOps, and release teams
Ensure testing alignment with CI/CD pipelines and enterprise quality standards
Act as a point of contact for QA during releases and production rollouts
Profile
Required Experience
5–10 years of experience in Manual Testing and/or Automation Testing (Automation preferred)
Proven experience in leading or mentoring QA teams
Strong experience in test design, execution, and maintenance
Solid knowledge of JUnit and TestNG frameworks
Hands-on experience in building or maintaining test automation frameworks
Experience working in Agile/Scrum environments
Preferred / Nice-to-Have Skills
Working knowledge of Tricentis Tosca (preferred, not mandatory)
Proficiency in Selenium and Core Java
Strong understanding of Object-Oriented Programming (OOP) concepts
Experience with version control systems (e.g., Git)
Knowledge of API test automation
Knowledge of Tosca Standard and Image-based identification strategies
Experience with Test Management tools such as Zephyr Scale or Xray
Exposure to CI/CD tools such as GitHub Actions and Jenkins
Experience in Investment Banking / Financial Services domain
Exposure to enterprise-scale testing and regulated environments
Soft Skills & Competencies
Strong leadership and team-player mindset
Excellent analytical and problem-solving skills
Clear and effective communication and stakeholder collaboration
Ability to work independently while driving team accountability
Proactive, detail-oriented, and quality-focused
Responsibilities
Lead Software Engineer - Test Automation - (25000JDZ)
Missions
Key Responsibilities
Lead and support change management activities across the product change and release management process
Define, implement, and own test strategies, test plans, and quality metrics
Hands-on involvement in manual and automation testing, with a focus on risk-based testing
Design, review, and maintain test cases, test scenarios, and automation frameworks
Guide and mentor QA team members on best practices, tools, and frameworks
Review test results, identify defects, perform root cause analysis, and drive defect resolution
Collaborate closely with developers, product owners, DevOps, and release teams
Ensure testing alignment with CI/CD pipelines and enterprise quality standards
Act as a point of contact for QA during releases and production rollouts
Profile
Required Experience
5–10 years of experience in Manual Testing and/or Automation Testing (Automation preferred)
Proven experience in leading or mentoring QA teams
Strong experience in test design, execution, and maintenance
Solid knowledge of JUnit and TestNG frameworks
Hands-on experience in building or maintaining test automation frameworks
Experience working in Agile/Scrum environments
Preferred / Nice-to-Have Skills
Working knowledge of Tricentis Tosca (preferred, not mandatory)
Proficiency in Selenium and Core Java
Strong understanding of Object-Oriented Programming (OOP) concepts
Experience with version control systems (e.g., Git)
Knowledge of API test automation
Knowledge of Tosca Standard and Image-based identification strategies
Experience with Test Management tools such as Zephyr Scale or Xray
Exposure to CI/CD tools such as GitHub Actions and Jenkins
Experience in Investment Banking / Financial Services domain
Exposure to enterprise-scale testing and regulated environments
Soft Skills & Competencies
Strong leadership and team-player mindset
Excellent analytical and problem-solving skills
Clear and effective communication and stakeholder collaboration
Ability to work independently while driving team accountability
Proactive, detail-oriented, and quality-focused
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
Role Category :Programming & Design
Role :Lead Software Engineer - Test Automation - (25000JDZ)
Strong expertise in Azure Data Services: o Azure Data Lake, Azure Databricks, Azure Synapse, Azure Data Factory. • Proficiency in Python and PySpark for large-scale data processing. • Solid understanding of data lakehouse architectures, Delta Lake, and parquet formats. • Experience with ETL/ELT design, data modeling, and pipeline orchestration. • Familiarity with SQL (T-SQL, Spark SQL) for querying and transformations. • Knowledge of CI/CD practices and source control (Azure DevOps, Git, GitHub). • Strong problem-solving skills with ability to debug complex data issues.
Responsibilities
Strong expertise in Azure Data Services: o Azure Data Lake, Azure Databricks, Azure Synapse, Azure Data Factory. • Proficiency in Python and PySpark for large-scale data processing. • Solid understanding of data lakehouse architectures, Delta Lake, and parquet formats. • Experience with ETL/ELT design, data modeling, and pipeline orchestration. • Familiarity with SQL (T-SQL, Spark SQL) for querying and transformations. • Knowledge of CI/CD practices and source control (Azure DevOps, Git, GitHub). • Strong problem-solving skills with ability to debug complex data issues.
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
SSE Bigdata Talend - (26000A43)
Missions
Design, develop, and maintain complex ETL pipelines using Talend Big Data components executed on the Spark framework.
Build and manage ETL solutions to ingest data from structured and unstructured sources.
Develop Talend Jobs, Joblets, and custom Java-based components.
Perform installation, configuration, and maintenance of Talend Job Server, TAC Server, and related components.
Optimize Talend jobs for performance, scalability, and parallel execution across multiple environments.
Deploy Talend jobs across environments and support automated deployment pipelines.
Create and manage Context Groups, parameterization frameworks, and custom routines.
Implement robust error handling, monitoring, logging, alerting, and reporting mechanisms.
Write and execute unit test cases and support integration testing.
Participate in performance tuning, troubleshooting, and best-practice recommendations.
Use Talend Administration Console (TAC) for job scheduling, deployment, and administration (advantage).
Provide operational support for data services, including production issue resolution and coordination with platform teams.
Profile
Must‑Have Skills
4+ years of hands-on experience with Talend Big Data ETL platform.
Strong experience developing Talend jobs on Spark-based architectures.
Strong SQL programming skills (preferably SQL Server).
Strong understanding of:
End-to-end ETL lifecycle and data integration fundamentals
Talend core components, transformations, orchestration, and optimization
Joblets, custom components, and Java-based custom logic
Error handling, monitoring, and performance tuning frameworks
Java debugging fundamentals, especially during TAC executions.
Excellent analytical, problem-solving, and troubleshooting skills.
Strong communication skills and ability to collaborate with cross-functional teams.
Big Data & Cloudera Platform Skills (Required)
Advanced development and optimization using Hive and Impala.
Strong hands-on experience with HDFS and Hadoop at the user level.
Solid working knowledge of Linux and command-line environments.
Experience in data modeling and data layout design on HDFS for analytical workloads.
Proven ability in performance tuning of:
Hive and Impala queries
Partitioning strategies and file formats (Parquet, ORC)
Operational support experience involving troubleshooting, monitoring, and coordination with Cloudera platform administrators.
Responsibilities
SSE Bigdata Talend - (26000A43)
Missions
Design, develop, and maintain complex ETL pipelines using Talend Big Data components executed on the Spark framework.
Build and manage ETL solutions to ingest data from structured and unstructured sources.
Develop Talend Jobs, Joblets, and custom Java-based components.
Perform installation, configuration, and maintenance of Talend Job Server, TAC Server, and related components.
Optimize Talend jobs for performance, scalability, and parallel execution across multiple environments.
Deploy Talend jobs across environments and support automated deployment pipelines.
Create and manage Context Groups, parameterization frameworks, and custom routines.
Implement robust error handling, monitoring, logging, alerting, and reporting mechanisms.
Write and execute unit test cases and support integration testing.
Participate in performance tuning, troubleshooting, and best-practice recommendations.
Use Talend Administration Console (TAC) for job scheduling, deployment, and administration (advantage).
Provide operational support for data services, including production issue resolution and coordination with platform teams.
Profile
Must‑Have Skills
4+ years of hands-on experience with Talend Big Data ETL platform.
Strong experience developing Talend jobs on Spark-based architectures.
Strong SQL programming skills (preferably SQL Server).
Strong understanding of:
End-to-end ETL lifecycle and data integration fundamentals
Talend core components, transformations, orchestration, and optimization
Joblets, custom components, and Java-based custom logic
Error handling, monitoring, and performance tuning frameworks
Java debugging fundamentals, especially during TAC executions.
Excellent analytical, problem-solving, and troubleshooting skills.
Strong communication skills and ability to collaborate with cross-functional teams.
Big Data & Cloudera Platform Skills (Required)
Advanced development and optimization using Hive and Impala.
Strong hands-on experience with HDFS and Hadoop at the user level.
Solid working knowledge of Linux and command-line environments.
Experience in data modeling and data layout design on HDFS for analytical workloads.
Proven ability in performance tuning of:
Hive and Impala queries
Partitioning strategies and file formats (Parquet, ORC)
Operational support experience involving troubleshooting, monitoring, and coordination with Cloudera platform administrators.
Salary : As per industry standard.
Industry :IT-Software / Software Services
Functional Area : IT Software - Application Programming , Maintenance
