lead AI Engineer

Lead AI Engineer – Generative AI & Agentic AI
Job Type: Full-Time
Experience: 6+ Years overall
Location: Remote

Job Overview

WebSenor is looking for a highly skilled Lead AI Engineer to lead the design and development of enterprise-grade Generative AI, Agentic AI, and LLM-based solutions.

The ideal candidate will have strong expertise in RAG architecture, AI platform architecture, LLMOps, vector search, semantic retrieval, and Python-based AI application development. The role will involve architecting scalable AI solutions, leading technical initiatives, integrating LLM platforms, and establishing responsible AI and governance practices.

Experience in the Healthcare or Insurance domain will be highly preferred.

Key Responsibilities

• Lead the architecture, design, and development of enterprise-grade Generative AI and Agentic AI solutions.
• Design and implement scalable RAG (Retrieval-Augmented Generation) architectures.
• Develop LLM-powered applications using Python and modern AI frameworks.
• Design intelligent AI agents and multi-agent workflows using frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, or CrewAI.
• Build vector search, semantic retrieval, and hybrid search solutions for enterprise knowledge systems.
• Design and optimize embedding pipelines, vector databases, knowledge graphs, and retrieval mechanisms.
• Work with Azure OpenAI, Azure AI Foundry, AWS Bedrock, Vertex AI, OpenAI, Anthropic, and other LLM platforms.
• Architect enterprise AI platforms and LLMOps pipelines for development, deployment, monitoring, evaluation, and lifecycle management.
• Integrate AI solutions with enterprise applications, APIs, data platforms, and business workflows.
• Evaluate and select appropriate LLMs, transformer models, embedding models, vector stores, and retrieval strategies based on business requirements.
• Develop scalable AI application architectures with focus on performance, security, reliability, and cost optimization.
• Establish AI governance, Responsible AI, security, privacy, and compliance practices.
• Implement monitoring, evaluation, observability, and continuous improvement for LLM-based applications.
• Collaborate with engineering, data, product, security, and business teams to translate business requirements into AI solutions.
• Provide technical leadership, conduct architecture reviews, mentor engineers, and establish development best practices.
• Stay current with advancements in LLMs, agentic systems, RAG, multimodal AI, AI platforms, and emerging GenAI technologies.

Required Skills

• Strong professional experience in AI/ML engineering, with significant hands-on experience in Generative AI and LLM-based application development.
• Strong expertise in RAG architecture, Agentic AI, LLM application architecture, and AI platform design.
• Strong programming skills in Python.
• Hands-on experience with one or more AI orchestration frameworks:
• LangChain
• LangGraph
• Semantic Kernel
• LlamaIndex
• AutoGen
• CrewAI
• Experience with vector databases and search technologies, such as:
• Chroma DB
• Azure AI Search
• Vector Search
• Hybrid Search
• Strong understanding of:
• Embeddings
• Semantic Retrieval
• Transformer Models
• Knowledge Graphs
• Prompt Engineering
• LLM Evaluation
• Experience with one or more major LLM/cloud AI platforms:
• Azure OpenAI
• Azure AI Foundry
• AWS Bedrock
• Google Vertex AI
• OpenAI
• Anthropic
• Experience designing and implementing LLMOps / MLOps practices.
• Strong understanding of enterprise AI integration, API-based architectures, security, governance, and Responsible AI.
• Strong system design, architecture, analytical, and problem-solving skills.
• Proven ability to lead technical teams and drive AI initiatives from concept through production.

Preferred Qualifications

• Experience in Healthcare, Insurance, or Healthcare Payer domains.
• Experience with enterprise knowledge management and document intelligence solutions.
• Experience implementing Knowledge Graph + RAG architectures.
• Experience with advanced hybrid retrieval and search optimization.
• Experience with AI/LLM security, privacy, compliance, and governance.
• Experience with cloud-native AI platforms and scalable distributed architectures.
• Experience with multimodal LLM applications.
• Experience with AI observability, model evaluation, guardrails, and producti…