Lead AI ML Engineer

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.