Lead AI ML Engineer

• Job Posted On
• 10-11-2026 12:36:26
• Job Code
• Job_304998

Experience Required

8 - 12 years

Location

• Chennai, Tamil Nadu, India (CHN)
  Job Summary
• Job Title : Senior AI/ML Engineer – Generative AI & Document Intelligence
• Location : Offshore (India)
• Experience : 8-12+ Years
• Customer : Patterson Companies
• Employment Type : Full-Time
• Role Summary

We are seeking a Senior AI/ML Engineer with deep expertise in Generative AI, Intelligent Document Processing, Large Language Models (LLMs), Vector Databases, and AI solution engineering.

The ideal candidate will have hands-on experience designing and implementing enterprise-grade AI solutions leveraging Google Document AI, Retrieval Augmented Generation (RAG), Vector Search, LLM orchestration frameworks, and continuous learning feedback loops.

This role will be instrumental in developing AI-powered document intelligence solutions, enterprise knowledge assistants, semantic search platforms, and business process automation capabilities.

• Key Responsibilities
• Generative AI Solution Development
• Design, build, and deploy enterprise Generative AI solutions.
• Develop AI-powered assistants, copilots, and knowledge retrieval systems.
• Implement Retrieval Augmented Generation (RAG) architectures.
• Integrate Large Language Models (LLMs) into enterprise applications.
• Optimize prompts, retrieval strategies, and grounding mechanisms.
• Build scalable AI services supporting production workloads.
• Intelligent Document Processing
• Design document ingestion and processing pipelines using Google Document AI.

Develop Solutions For

• OCR
• Document Classification
• Data Extraction
• Entity Recognition
• Form Processing
• Invoice Processing
• Contract Intelligence
• Build automated workflows to process structured and unstructured documents.
• Vector Database & Semantic Search
• Design and implement vector search solutions.
• Generate and manage embeddings for enterprise knowledge repositories.
• Build semantic search and contextual retrieval frameworks.
• Optimize chunking, indexing, and ranking mechanisms.
• Manage large-scale vector indexes and retrieval systems.
• Continuous Learning & AI Optimization
• Implement Human-in-the-Loop (HITL) feedback mechanisms.
• Design continuous learning pipelines to improve AI responses.
• Monitor AI model performance and user satisfaction metrics.

Build Evaluation Frameworks For

• Hallucination Detection
• Response Quality
• Retrieval Accuracy
• Grounding Effectiveness
• Drive model refinement through feedback loops and usage analytics.
• AI Platform Engineering
• Develop scalable AI APIs and microservices.
• Integrate AI services with enterprise applications.
• Collaborate with Data Engineers and Full Stack Developers.
• Support cloud-native AI deployments.
• Establish AI governance, monitoring, and operational best practices.