Senior AI/ML Engineer — RAG & Production Systems

Tata Consultancy Services; Chicago (IL); On-site; USD 120,000 - 130,000; Full time

Benefits offered by this job

Discretionary Annual Incentive

Comprehensive Medical Coverage

401K Plan

Certification & Training Reimbursement

Vacation, Time Off, Holidays

Job summary

Tata Consultancy Services in Chicago is seeking an advanced AI/ML engineer to design, build, and deploy AI/ML solutions across the full lifecycle from experimentation to production.

You will develop ML models and LLM-powered RAG/agent systems, design embeddings and orchestration patterns, own end-to-end implementation, deployment, and operations, and champion responsible AI governance.

Qualifications

• Bachelor's degree in Computer Science or related field.
• Experience with Databricks and Azure AI Foundry in regulated environments.
• Strong background in ML engineering, production deployment, and MLOps.

Responsibilities

• Design, build, and deploy AI/ML solutions.
• Lead full AI lifecycle from experimentation to production.
• Build ML models and LLM-powered RAG/agent systems.
• Translate business challenges into AI solutions and KPIs.
• Design embeddings/vector search and orchestration patterns.
• Own end-to-end implementation, deployment, and operations.
• Evaluate performance with automated and human-in-the-loop methods.
• Optimize latency, cost, scalability, reliability.
• Implement CI/CD, MLOps, monitoring, drift detection, retraining.
• Mentor and promote responsible AI governance.

Skills

Databricks

Azure AI Foundry

ML engineering

production ML deployment

RAG systems

Agentic AI

Python

FastAPI/MLflow

vector databases

embeddings

LangChain

CI/CD

containerization

cloud deployment

MLOps

model monitoring

governance

Education

Bachelor of Computer Science

Job description

Tata Consultancy Services in Chicago is seeking an advanced AI/ML engineer to design, build, and deploy AI/ML solutions across the full lifecycle from experimentation to production.

You will develop ML models and LLM-powered RAG/agent systems, design embeddings and orchestration patterns, own end-to-end implementation, deployment, and operations, and champion responsible AI governance.