AI/ML engineer

Tata Consulting Services, PLC; Bengaluru, Pune District; On-site; INR 1,080,000 - 1,320,000; Full time

Job summary

Tata Consulting Services, PLC is seeking a skilled MLOps engineer to take production-ready ML models from notebooks to enterprise deployments. You will own model serving, monitoring, and the evaluation discipline that keeps models honest in production.

You will collaborate with client data science teams to ensure operational transfer and alignment with SLAs, while building robust offline and online evaluation suites to detect drift early.

Qualifications

• 2+ years shipping ML in production.
• Experience taking at least one model beyond a notebook – training pipelines, serving, monitoring.
• Strong Python and modern ML stack experience (PyTorch or TensorFlow) with tooling.

Responsibilities

• Take ML models from notebooks to production systems with MLOps, model serving, and monitoring.
• Build and maintain evaluation suites to detect drift before clients.
• Partner with client data science teams to transfer operational discipline.

Skills

Python

Production ML

ML pipelines

Model monitoring

Team collaboration

Tools

PyTorch

TensorFlow

MLflow

Airflow

Kubeflow

SageMaker

Vertex AI

Job description

You'll take machine learning models from a data scientist's notebook to production - MLOps, model serving, and the evaluation discipline that keeps them honest.

About the role.

You'll work alongside our AI & Data practice to take models from a data scientist's notebook into production systems that enterprise clients actually run - which means MLOps, model serving, monitoring, and the evaluation discipline that catches drift before a client does.

Clients bring genuinely hard problems: fraud detection at transaction-time latency, demand forecasting across volatile supply chains, document extraction pipelines that have to be right, not just plausible.

Turn research-quality models into services with defined SLAs - versioned, monitored, and rollback-able like any other production system.

Evaluate

Own the evaluation harness

Build and maintain the offline and online evaluation suites that tell us - before the client does - when a model's performance has degraded.

Partner

Work directly with client data science teams

Pair with client-side practitioners to transfer the operational discipline, not just hand over a deployed endpoint.

What you'll bring

What we’re looking for.

Must have

• 2+ years shipping ML in production
• Experience taking at least one model beyond a notebook - training pipelines, serving infrastructure, monitoring.

Must have

• Strong Python
• Comfortable across the modern ML stack - PyTorch or TensorFlow, plus the surrounding tooling (MLflow, Airflow, or equivalents).

Preferred

• MLOps platform experience
• Kubeflow, SageMaker, Vertex AI, or a comparable production ML platform.

Preferred

• Domain exposure
• Prior work in fraud, forecasting, or document/NLP pipelines is a strong plus.