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Data Engineer

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TerraGiG

India, Tamil Nadu, Chennai Full-Time On-site INR 4–7 LPA

Posted 5 hours ago • Apply by June 14, 2026

Job Description

Job Title: Senior Gen AI Developer – Generative & Agentic AI, and Data Science

Experience Required: 5–7 Years

Location: Chennai

About the Role

We are looking for an experienced Senior Gen AI developer with Data Scientist background and a strong foundation in machine learning, deep learning, and modern Generative AI systems, with hands-on experience in model deployment.

This role involves designing and implementing AI-driven solutions that combine traditional ML with LLMs and agentic AI frameworks to power intelligent automation, document understanding, and enterprise-grade AI assistants.

Key Responsibilities

1. Generative AI

Design and fine-tune LLMs (GPT, Claude, Llama, Mistral, etc.) for domain-specific use cases such as document understanding, summarization, and conversational AI.
Implement retrieval-augmented generation (RAG) pipelines using vector databases (FAISS, Pinecone, Qdrant, Weaviate) and enterprise knowledge bases.
Develop prompt engineering strategies and evaluate outputs for factual accuracy, coherence, and alignment.
Experiment with multimodal and diffusion models for image, audio, and text synthesis tasks.

2. Agentic AI Systems

Build and orchestrate multi-agent frameworks for dynamic decision-making and workflow automation.
Integrate AI agents with enterprise APIs, databases, and third-party systems to enable autonomous task execution.
Utilize frameworks such as LangChain, CrewAI, Semantic Kernel, and AutoGen to develop production-ready AI agents.
Prototype and scale AI copilots and enterprise automation agents for business teams.

3. Core Data Science & ML

Build, train, and optimize predictive and prescriptive models across regression, classification, and forecasting use cases.
Perform feature engineering, model evaluation, and interpretability analysis using frameworks like scikit-learn, PyTorch, TensorFlow, or XGBoost.
Conduct statistical analysis and A/B experiments to validate data-driven decisions.
Develop MLOps pipelines for scalable and automated model training and deployment.
Work closely with data engineering teams to prepare high-quality, production-ready datasets from S3, and any of the Redshift, Snowflake, BigQuery, or Synapse.
Airflow, or Data Factory for ML and GenAI workflows.
Ensure data lineage, governance, and quality checks for reproducible model outputs.
Deploy AI models in cloud environments with SageMaker, Vertex AI, or Azure ML.

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