Senior AI Engineer

Job Description – Senior AI Engineer (GenAI | Python | AWS Bedrock)

Location: Remote – India

Duration: 6 Months (Contract)

Overall Experience: 5+ Years

Relevant GenAI Experience: 5+ Years

Role Overview

• We are looking for a highly experienced Senior AI Engineer with 5+ years of hands-on experience in Generative AI (GenAI), Large Language Models (LLMs), and AI application development.
• The candidate will be responsible for designing, developing, and optimizing production-grade GenAI solutions for document intelligence, automated document comparison, change detection, classification, and AI-powered chatbot applications.
• The ideal candidate must have strong hands-on expertise in Python, Amazon Bedrock, Retrieval-Augmented Generation (RAG), vector databases, embeddings, and prompt engineering. The role requires someone capable of independently developing complex GenAI pipelines, implementing advanced RAG techniques, improving LLM output quality, and delivering scalable AI-powered applications.

Key Responsibilities

• Design, develop, and implement production-grade Generative AI applications using Python, Amazon Bedrock, and Large Language Models (LLMs).
• Build and optimize end-to-end RAG pipelines, including document ingestion, chunking, embedding generation, indexing, retrieval, and response generation.
• Develop document intelligence solutions to compare different document or policy versions, identify meaningful changes, align sections and criteria, and eliminate irrelevant formatting or content differences.
• Implement LLM-based classification pipelines using Amazon Bedrock and rule-based approaches to categorize document changes into predefined change types.
• Develop AI-powered chatbot solutions capable of answering user queries using retrieved document context, with accurate source references and citations.
• Implement advanced RAG techniques such as re-ranking, hybrid search, confidence scoring, Self-RAG, and retrieval optimization to improve response accuracy and reduce hallucinations.
• Design, develop, and optimize prompt templates for document summarization, change detection, classification, and chatbot applications.
• Work with Amazon Bedrock Knowledge Bases and Amazon OpenSearch / OpenSearch Serverless for vector storage, semantic search, and retrieval.
• Integrate GenAI applications with AWS services, including Lambda and Step Functions, to develop scalable AI workflows.
• Integrate structured and unstructured data sources and collaborate with Data Engineers on JSON schemas, document structures, and data processing pipelines.
• Perform model evaluation, unit testing, debugging, and performance optimization to improve AI application quality, accuracy, and reliability.
• Troubleshoot LLM-related issues, including hallucinations, inaccurate classifications, low-confidence responses, and retrieval-quality problems.
• Collaborate with solution architects, data engineers, and testing teams to deliver production-ready GenAI solutions.
• Conduct code reviews, provide technical guidance, and mentor junior engineers.

Mandatory Technical Skills

• Generative AI (GenAI): Minimum 5+ years of hands-on experience developing Generative AI and LLM-based applications.
• Python: Strong programming experience in Python, including application development, API integration, debugging, and testing.
• Large Language Models (LLMs): Strong understanding and practical experience working with LLMs, foundation models, and AI-powered applications.
• Amazon Bedrock: Hands-on experience using Amazon Bedrock foundation models to build and deploy GenAI solutions.
• Retrieval-Augmented Generation (RAG): Strong implementation experience with end-to-end RAG pipelines, document ingestion, chunking, embeddings, retrieval, and context augmentation.
• Vector Databases: Hands-on experience with Amazon OpenSearch, OpenSearch Serverless, or similar vector databases.
• Prompt Engineering: Experience designing, testing, and optimizing prompts for summarization, classification, information extraction, and conversational AI.
• Document Intelligence: Experience with document processing, NLP classification, information extraction, document comparison, or automated document intelligence solutions.
• AWS Services: Working knowledge of AWS Lambda and familiarity with Step Functions or similar workflow orchestration services.

Good-to-Have Skills

• Experience with Amazon Bedrock Knowledge Bases and Bedrock AgentCore.
• Hands-on experience with LangChain, LangGraph, or similar LLM orchestration frameworks.
• Knowledge of advanced RAG techniques, including re-ranking, hybrid search, Self-RAG, and confidence-based retrieval.
• Experience implementing source citations, grounding, and hallucination-reduction mechanisms.
• Exposure to multimodal LLMs, OCR, and document extraction pipelines.
• Experience integrating Snowflake or other structured data platforms with GenAI applications.
• Experience with LLM evaluation, model performance tuning, and production deployment.
• Experience leading technical implementations, conducting code reviews, and mentoring engineering teams.

Ideal Candidate Profile

• Senior AI Engineer with 5+ years of hands-on GenAI development experience.
• Strong practical experience building and deploying production-grade LLM and RAG applications.
• Expertise in Python, Amazon Bedrock, vector databases, embeddings, and prompt engineering.
• Experience developing document intelligence, automated classification, and AI-powered chatbot solutions.
• Ability to independently troubleshoot complex GenAI challenges and optimize model performance.
• Strong problem-solving, communication, and technical collaboration skills.