> Markdown version of [/jobs/ext/571914-senior-ai-ml-engineer](https://www.wearedevelopers.com/jobs/ext/571914-senior-ai-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior AI/ML Engineer

- **Company:** Tata Consultancy Services Limited
- **Location:** Eden Prairie, MN, United States
- **Experience:** Expert
- **Salary:** $100,000.0 - $120,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Automation of Tests, Continuous Integration, Information Engineering, Information Leak Prevention, DevOps, Python (Programming Language), Machine Learning, Systems Development Life Cycle, Tensorflow, Azure Machine Learning, Software Engineering, Management of Software Versions, Pytorch, Kubernetes, Information Technology, Deployment Automation, Machine Learning Operations, Api Design, Docker
- **Published:** June 20, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=fe03160d5b7f3be3

## About the Role

Do you have experience in System design for system development?, Do you have a Bachelor's degree?, Builds, trains and tunes machine learning models. Translates data science experiments into scalable, production-ready ML solutions., * 5+ years software engineering with 2+ years shipping ML models to production.

 * Strong Python skills and experience with ML frameworks (TensorFlow/PyTorch).

 * Experience with containers and orchestration (Docker/Kubernetes) and API development.

 * Understanding of ML system design (data leakage, training-serving skew, drift).

 * CI/CD and DevOps practices applied to ML workloads (MLOps)., Qualifications : BACHELOR OF COMPUTER SCIENCE

## Description

Translate data science prototypes into production-grade ML services and pipelines.

 * Build training and inference code with reproducibility, versioning, and automated testing.

 * Implement scalable model serving (online/offline), batching, and latency/throughput optimization.

 * Integrate model lifecycle tooling (tracking, registry, deployment automation, monitoring).

 * Collaborate with Data Engineering on feature pipelines and data contracts.

 * Own production health: drift detection, performance regression, rollback strategies, and incident response.


## Related Videos

- [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning)
- [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered)
- [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform)
- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps)
- [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)