Lead Gen AI Engineer

GAVS Technologies N.A., Inc; Chennai District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

GAVS Technologies N.A., Inc. in Chennai, India, seeks an experienced AI/ML engineer to translate business requirements into scalable GenAI solutions, leveraging Databricks, Azure AI, and Snowflake to deliver enterprise-grade AI capabilities.

You will architect end-to-end GenAI architectures, deploy agentic workflows with LangChain and OpenAI SDK, lead LLM experimentation, and ensure robust MLOps and governance. Strong background in NLP, multi-modal AI, and security practices is essential.

Qualifications

• Bachelor’s or Master’s degree in CS, Engineering, or related field.
• 8–11 years of AI/ML engineering experience with at least 3 years focused on GenAI/LLMs.
• Proven experience deploying agentic AI systems in production environments.
• Strong understanding of NLP, deep learning, and multi-modal AI.
• Experience with enterprise-grade AI governance and security practices.

Responsibilities

• Translate business requirements into scalable ML pipelines and AI solutions.
• Architect and implement scalable GenAI solutions using Azure/ GCP AI, Databricks, and Snowflake.
• Develop and deploy agentic workflows using LangChain, LangGraph, and OpenAI Agents SDK for autonomous task execution.
• Lead experimentation and fine-tuning of LLMs for enterprise use cases such as summarization, personalization, and content generation.
• Integrate GenAI models into business applications with HITL validation and feedback loops.
• Build and maintain MLOps/LLMOps pipelines using MLflow, ONNX, and Unity Catalog for reproducibility and governance.
• Monitor model performance and ensure responsible AI operations through observability tools.
• Stay current with GenAI and LLM advancements, including LangChain, LlamaIndex, and Gemini.

Skills

GenAI

LLMs

AI engineering

NLP

Security governance

Production deployment

MLOps

Multi-modal AI

Education

Bachelor’s or Master’s degree in Computer Science or Engineering

Tools

Databricks

Azure AI

Snowflake

LangChain

LangGraph

OpenAI SDK

Hugging Face

Vertex AI

MLflow

ONNX

Unity Catalog

Job description

Duties & Responsibilities

Translate business requirements into scalable and well-documented ML pipelines and AI solutions using Databricks, Azure AI, and Snowflake.

Architect and implement scalable GenAI solutions using Azure/ GCP AI, Databricks, and SnowflakeDevelop and deploy agentic workflows using LangChain, LangGraph, and OpenAI Agents SDK for autonomous task execution.

Lead experimentation and fine-tuning of LLMs (e.g., GPT-4, Claude, LLaMA 2) for enterprise use cases such as summarization, personalization, and content generation.

Integrate GenAI models into business applications with Human-in-the-Loop (HITL) validation and feedback loops.

Build and maintain MLOps/LLMOps pipelines using MLflow, ONNX, and Unity Catalog for reproducibility and governance.

Monitor model performance and ensure responsible AI operations through observability tools like OpenTelemetry and Databricks AI Gateway.

Stay current with GenAI and LLM advancements, including frameworks like LangChain, LlamaIndex, and Gemini, and apply them to enterprise use cases.

Basic Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 8–11 years of experience in AI/ML engineering, with at least 3 years focused on GenAI and LLMs.
• Proven experience deploying agentic AI systems in production environments.
• Strong understanding of NLP, deep learning, and multi-modal AI (text, image, audio).
• Experience with enterprise-grade AI governance and security practices.

Preferred Qualifications

• Languages: Python, SQL, PySpark
• Agent frameworks: LangChain, LangGraph, Hugging Face, OpenAI SDK, Gemini
• GenAI Tools: Azure AI Foundry, Vertex AI, Databricks AI
• MLOps/LLMOps: MLflow, ONNX, Unity Catalog
• Data Platforms: Databricks, Snowflake, Data Lake, Knowledge graphs
• Understanding of AI governance, including model explainability, fairness, and security (e.g., prompt injection, data leakage mitigation)