Posted 06 July, 2026
Software Engineer IV
First Tek, Inc.
Bengaluru,Karnataka,India,560103
Full Time
Reference: 365_463711_26-00927
Description:
Work Timings: 01:30 - 10:30 PM
Monday (WFH), Tuesday-Friday(WFO)
Role Summary
Experience Range - 8-10 years
We are seeking a Software Engineer with strong Python and MLOps foundations to build, enhance, and stabilize a productiongrade AI/ML platform supporting model training, validation, deployment, and orchestration at enterprise scale.
This role goes beyond scripting or notebookdriven ML. You will work in a classbased, APIdriven, CI/CDenforced MLOps ecosystem, contributing to reusable libraries, ML workflows, secure endpoints, JSONschemadriven interfaces, and automated pipelines aligned with Chevron's evolving GitHub Actions strategy.
Key Responsibilities
Core Engineering & MLOps
- Design, build, and maintain productiongrade Python services using sound objectoriented principles (SOLID, separation of concerns, reusability).
Contribute to and extend the enterprise MLOps pipeline supporting:
- Model training, validation, and registration
- Azure ML-based execution
- Parameterized workflow orchestration
- CI/CDdriven deployments
Implement and maintain ML inference endpoints, with focus on:
-Highperformance I/O using async programming and concurrency
-Clean request/response contracts driven by JSON schemas
-Robust validation and error handling
Develop welldefined APIs (REST) with:
-OpenAPI / Swagger documentation
-Versionaware schemas and backward compatibility considerations
Support platform evolution from Azure Pipelines to GitHub Actions, contributing to:
-Pipeline rearchitecture
-Build, test, and release automation
-Secure artifact promotion across environments
Data, ML & Storage
Work with Pandas and Polars for feature handling, transformations, and data preparation.
Support ML workflows leveraging ScikitLearn models and pipelines.
Integrate with Azure Blob Storage and Azure Data Lake for model artifacts, datasets, and metadata.
(Nice to have) Contribute to solutions involving Azure Cosmos DB for metadata or workflow state tracking.
Quality, Testing & Maintainability
Write clean, maintainable, and testable code-optimizing for longterm platform health over shortterm delivery speed.
Expand and strengthen the automated test suite, including:
-PyTestbased unit and integration tests
-Validation of pipelines, schemas, and services
Advocate for and gradually adopt TestDriven Development (TDD) practices across the codebase.
Actively reduce technical debt in a legacyheavy environment by:
-Refactoring duplicated or brittle code
-Introducing shared libraries and abstractions
-Improving documentation and developer ergonomics
Required Technical Skills
Advanced Python
-Classbased design, modular architecture, reusable components
-Not "scriptonly" or notebookcentric development
API Development
-RESTful services, endpoint performance tuning
-OpenAPI / Swagger documentation
Async Programming
-Async/await, concurrency, threading for I/Oheavy workloads
CI/CD
-Azure Pipelines and/or GitHub Actions
-Experience evolving pipelines, not just consuming them
Data & ML
-Pandas / Polars
-ScikitLearn
Testing
-PyTest
-Strong appreciation for automated testing as a quality gate
SchemaDriven Development
-JSON schemas for configuration, workflow parameters, and APIs
-Experience updating and managing schema evolution
NicetoHave Skills
Azure ML SDK (training, pipelines, model registration)
Pydantic for request/response validation
Azure Cosmos DB
Schema versioning strategies
Experience with large, shared enterprise codebases
Desired Qualities & Mindset
Takes Initiative
-Understands how individual stories connect to platformlevel objectives.
-Proactively improves systems instead of waiting for direction.
Strong Debugger
-Comfortable tracing failures across pipelines, services, storage, and infrastructure.
-Attempts rootcause analysis before escalating issues.
Values Code Quality
-Will not compromise maintainability for speed.
-Actively resists adding to technical debt.
Engineering Craftsmanship
-Cares about design, clarity, and longterm scalability.
-Sees MLOps as software engineering, not just ML enablement.
Advocates for Better Practices
-Encourages testability, consistency, and clean abstractions-even in legacy environments.
Work Timings: 01:30 - 10:30 PM
Monday (WFH), Tuesday-Friday(WFO)
Role Summary
Experience Range - 8-10 years
We are seeking a Software Engineer with strong Python and MLOps foundations to build, enhance, and stabilize a productiongrade AI/ML platform supporting model training, validation, deployment, and orchestration at enterprise scale.
This role goes beyond scripting or notebookdriven ML. You will work in a classbased, APIdriven, CI/CDenforced MLOps ecosystem, contributing to reusable libraries, ML workflows, secure endpoints, JSONschemadriven interfaces, and automated pipelines aligned with Chevron's evolving GitHub Actions strategy.
Key Responsibilities
Core Engineering & MLOps
- Design, build, and maintain productiongrade Python services using sound objectoriented principles (SOLID, separation of concerns, reusability).
Contribute to and extend the enterprise MLOps pipeline supporting:
- Model training, validation, and registration
- Azure ML-based execution
- Parameterized workflow orchestration
- CI/CDdriven deployments
Implement and maintain ML inference endpoints, with focus on:
-Highperformance I/O using async programming and concurrency
-Clean request/response contracts driven by JSON schemas
-Robust validation and error handling
Develop welldefined APIs (REST) with:
-OpenAPI / Swagger documentation
-Versionaware schemas and backward compatibility considerations
Support platform evolution from Azure Pipelines to GitHub Actions, contributing to:
-Pipeline rearchitecture
-Build, test, and release automation
-Secure artifact promotion across environments
Data, ML & Storage
Work with Pandas and Polars for feature handling, transformations, and data preparation.
Support ML workflows leveraging ScikitLearn models and pipelines.
Integrate with Azure Blob Storage and Azure Data Lake for model artifacts, datasets, and metadata.
(Nice to have) Contribute to solutions involving Azure Cosmos DB for metadata or workflow state tracking.
Quality, Testing & Maintainability
Write clean, maintainable, and testable code-optimizing for longterm platform health over shortterm delivery speed.
Expand and strengthen the automated test suite, including:
-PyTestbased unit and integration tests
-Validation of pipelines, schemas, and services
Advocate for and gradually adopt TestDriven Development (TDD) practices across the codebase.
Actively reduce technical debt in a legacyheavy environment by:
-Refactoring duplicated or brittle code
-Introducing shared libraries and abstractions
-Improving documentation and developer ergonomics
Required Technical Skills
Advanced Python
-Classbased design, modular architecture, reusable components
-Not "scriptonly" or notebookcentric development
API Development
-RESTful services, endpoint performance tuning
-OpenAPI / Swagger documentation
Async Programming
-Async/await, concurrency, threading for I/Oheavy workloads
CI/CD
-Azure Pipelines and/or GitHub Actions
-Experience evolving pipelines, not just consuming them
Data & ML
-Pandas / Polars
-ScikitLearn
Testing
-PyTest
-Strong appreciation for automated testing as a quality gate
SchemaDriven Development
-JSON schemas for configuration, workflow parameters, and APIs
-Experience updating and managing schema evolution
NicetoHave Skills
Azure ML SDK (training, pipelines, model registration)
Pydantic for request/response validation
Azure Cosmos DB
Schema versioning strategies
Experience with large, shared enterprise codebases
Desired Qualities & Mindset
Takes Initiative
-Understands how individual stories connect to platformlevel objectives.
-Proactively improves systems instead of waiting for direction.
Strong Debugger
-Comfortable tracing failures across pipelines, services, storage, and infrastructure.
-Attempts rootcause analysis before escalating issues.
Values Code Quality
-Will not compromise maintainability for speed.
-Actively resists adding to technical debt.
Engineering Craftsmanship
-Cares about design, clarity, and longterm scalability.
-Sees MLOps as software engineering, not just ML enablement.
Advocates for Better Practices
-Encourages testability, consistency, and clean abstractions-even in legacy environments.
