Job Description :-
Job Title: Lead AI Engineer
Experience: 8+ Years
Employment Type: Full-Time
Work Mode: Remote
Job Summary
We are looking for an experienced Lead AI Engineer with strong expertise in Generative AI, Agentic AI, RAG Architecture, LLM-based solutions, and Enterprise AI platforms.
The ideal candidate will be responsible for designing, developing, and leading scalable AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Vector Search, Semantic Retrieval, AI Agents, and LLMOps.
This role requires a hands-on technical leader who can architect enterprise-grade AI applications, integrate AI capabilities with existing business systems, establish AI governance and responsible AI practices, and guide engineering teams in delivering production-ready AI solutions.
Key Responsibilities
- Lead the architecture, design, and development of Generative AI and Agentic AI solutions for enterprise applications.
- Design and implement scalable RAG architectures using vector databases, semantic search, embeddings, knowledge graphs, and hybrid retrieval.
- Develop LLM-powered applications, AI agents, copilots, and intelligent automation solutions.
- Design multi-agent workflows using frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, and CrewAI.
- Build and optimize semantic retrieval and vector search pipelines for enterprise knowledge bases.
- Work with LLMs, transformer models, embeddings, prompt engineering, fine-tuning, and model evaluation.
- Architect and implement enterprise AI platforms and LLM application architectures.
- Develop production-grade AI applications using Python and modern AI/ML frameworks.
- Implement LLMOps/MLOps practices covering model deployment, monitoring, evaluation, observability, versioning, and lifecycle management.
- Integrate enterprise AI solutions with cloud platforms and business applications.
- Work with cloud AI services including Azure OpenAI, Azure AI Foundry, AWS Bedrock, and Google Vertex AI.
- Design AI solutions using Azure AI Search, Chroma DB, vector databases, knowledge graphs, and hybrid search.
- Evaluate and integrate models from providers such as OpenAI, Anthropic, and other leading LLM platforms.
- Establish best practices for AI security, governance, responsible AI, data privacy, and model risk management.
- Collaborate with architects, data engineers, software engineers, product teams, and business stakeholders to translate business requirements into AI solutions.
- Lead technical discussions, architecture reviews, proof-of-concepts, and AI solution design initiatives.
- Mentor AI engineers and provide technical guidance on Generative AI and Agentic AI development.
- Drive AI solutions from POC through production deployment and enterprise-scale adoption.
Primary Skills
- Generative AI
- Agentic AI
- RAG Architecture
- LLM-Based Solutions
- Vector Search
- Semantic Retrieval
- AI Platform Architecture
- LLMOps
- Python
- AI Application Development
- AI Governance
- Enterprise AI Integration
Secondary Skills
- LangChain
- LangGraph
- Semantic Kernel
- LlamaIndex
- AutoGen
- CrewAI
- Azure OpenAI
- Azure AI Foundry
- AWS Bedrock
- Google Vertex AI
- OpenAI
- Anthropic
- Chroma DB
- Azure AI Search
- Knowledge Graphs
- Hybrid Search
- Transformer Models
- Embeddings
- Responsible AI
- Healthcare / Insurance Domain
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field.
- 8+ years of experience in software engineering, AI/ML engineering, or related technical roles.
- Strong hands-on experience building Generative AI and LLM-based applications.
- Proven experience designing and implementing RAG-based enterprise solutions.
- Strong programming skills in Python.
- Experience with vector databases, semantic search, embeddings, and retrieval architectures.
- Experience working with one or more major cloud AI platforms such as Azure, AWS, or GCP.
- Strong understanding of LLM architecture, transformer models, prompt engineering, context management, and model evaluation.
- Experience taking AI solutions from proof-of-concept to production.
- Strong understanding of enterprise application integration, APIs, security, and scalability.
Preferred Qualifications
- Experience developing multi-agent AI systems.
- Hands-on experience with LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, or CrewAI.
- Experience with Azure AI Foundry and Azure OpenAI.
- Experience implementing hybrid search, knowledge graphs, and advanced retrieval strategies.
- Experience with LLMOps, AI observability, model evaluation, and production monitoring.
- Knowledge of Responsible AI, AI governance, security, and compliance.
- Experience in Healthcare or Insurance domain is highly preferred.
- Experience leading technical teams or mentoring AI engineers.
Key Competencies
- AI Solution Architecture
- Generative AI Engineering
- Agentic AI Development
- RAG & Semantic Search
- LLM Application Development
- AI Platform Engineering
- Cloud AI Architecture
- Enterprise Integration
- AI Governance & Responsible AI
- Technical Leadership
- Problem Solving & Innovation
Work Location: Remote
