AIML Senior Engineer

Tata Consultancy Services · IT Consulting & Digital Transformation · IT Outsourcing & Dedicated Teams · Bachelor's Degree · Python · SQL · ETL/ELT · 4 days ago
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Location
In office (Bengaluru)
Seniority
Senior · 5+ years exp

Confirmed on the employer's own hiring board on Nov 6, 2026. First seen by Alion on Nov 2, 2026. Tata Consultancy Services scores A on the Alion truth index.

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Tata Consultancy Services

Tata Consultancy Services (TCS) is an Indian IT services and consulting company headquartered in Mumbai and part of the Tata Group. Founded in 1968, it is listed on the BSE and the National Stock Exchange of India and is one of the largest IT services companies in the world by revenue and headcount, with about 600,000 employees. It provides application development and maintenance, cloud and infrastructure services, enterprise platform implementation, cybersecurity, consulting and business process services to large enterprises across North America, Europe and Asia-Pacific.
tcs.com · HQ: Mumbai, India · Management Consulting · Systems Integrators · IT Consulting & Digital Transformation · IT Outsourcing & Dedicated Teams · Est. 1968
Tech stack of Tata Consultancy Services

Role: Engineer. Skills: Artificial Intelligence, Python. Experience: 5 - 10 Years. Qualification: Bachelor Of Engineering

Required Technical Skillset:-

1.Programming: Python (strong), SQL

2.ML Frameworks: Scikit learn, TensorFlow / PyTorch

3.GenAI / LLMs: OpenAI / Azure OpenAI, LangChain, vector databases

4.Data Engineering: ETL pipelines, data modeling, data validation

5.Cloud: Azure / AWS / GCP (at least one)

Must Have Skills:-

1.Programming: Python (strong), SQL

2.ML Frameworks: Scikit learn, TensorFlow / PyTorch

3.GenAI / LLMs: OpenAI / Azure OpenAI, LangChain, vector databases

4.Data Engineering: ETL pipelines, data modeling, data validation

5.Cloud: Azure / AWS / GCP (at least one)

6.Design and maintain ETL / ELT pipelines for large-scale data ingestion

7.Work with structured and semi-structured data (CSV, JSON, Parquet)

8.Ensure data quality, lineage, and reliability

9.Optimize pipelines for performance and scalability

10.Deploy ML/AI models using APIs or batch pipelines

11.Implement CI/CD for ML workflows

12.Monitor model performance, drift, and data issues

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