Lead AI Engineer

Job Title: Lead AI Engineer Location: Raleigh NC/ Foster City, CA Work Model: Hybrid and Remote About the Role We are seeking a hands-on Lead AI Engineer to lead the design, development, and deployment of enterprise-grade Artificial Intelligence and Generative AI solutions on AWS and modern cloud platforms. This role goes beyond experimentation, you will architect secure, scalable, cost-efficient, and production-ready AI systems leveraging Large Language Models (LLMs) , Retrieval-Augmented Generation (RAG) , AI Agents , semantic search , and foundation models . You will bridge the gap between cutting-edge AI research and practical business applications while ensuring governance, security, performance, and compliance. You will collaborate closely with Data Science, Engineering, Product, Security, and MLOps teams to deliver next-generation AI-powered applications , including intelligent assistants, document intelligence platforms, enterprise search systems, and autonomous AI workflows. Key Responsibilities AI & Solution Architecture Architect and implement enterprise-scale AI and Generative AI solutions using LLMs such as GPT, Claude, Llama, Mixtral, and other foundation models. Design end-to-end AI system architectures including data pipelines, model orchestration, API integrations, vector search layers, and application services. Design and deploy Retrieval-Augmented Generation (RAG) pipelines for document intelligence, enterprise search, knowledge management, and conversational AI applications. Build AI-powered assistants, chatbots, autonomous agents, and intelligent workflow automation solutions. Develop semantic search and embedding pipelines using vector databases and retrieval frameworks. Model Strategy & Optimization Evaluate, benchmark, and select appropriate foundation models based on business requirements, performance, cost, and scalability. Determine optimal approaches including prompt engineering, RAG, fine-tuning, LoRA, QLoRA, and PEFT techniques. Optimize prompts, inference strategies, token consumption, latency, throughput, and overall system performance. Implement hallucination mitigation techniques through grounding, validation layers, guardrails, and prompt constraints. Security, Governance & Responsible AI Design secure AI architectures with strong emphasis on data privacy, compliance, and enterprise security. Implement guardrails to prevent prompt injection, data leakage, misuse, and model vulnerabilities. Establish Responsible AI practices, bias mitigation strategies, explainability frameworks, and governance standards. Ensure compliance with enterprise and regulatory requirements including HIPAA, GDPR, and industry-specific standards where applicable. Cloud & Platform Engineering Design scalable AI solutions on AWS leveraging services such as Bedrock, SageMaker, Lambda, API Gateway, S3, DynamoDB, IAM, KMS, VPC, and CloudTrail. Architect containerized and cloud-native AI platforms using Docker and Kubernetes. Design multi-tenant AI architectures and enterprise-grade deployment models. Optimize infrastructure for performance, availability, scalability, and cost efficiency. MLOps / LLMOps Establish CI/CD pipelines for AI and ML systems. Implement model versioning, monitoring, observability, drift detection, rollback strategies, and automated deployment workflows. Define evaluation frameworks, logging standards, performance metrics, and operational monitoring practices. Collaborate with MLOps teams to productionize and operationalize AI solutions at scale. Collaboration & Leadership Partner with Engineering, Data Science, Product, Security, and Business teams to translate complex business requirements into scalable AI solutions. Provide technical leadership and architectural guidance across AI initiatives. Mentor engineers and promote AI best practices, innovation, and engineering excellence. Align AI capabilities with measurable business outcomes and strategic objectives. Required Skills & Experience Generative AI & Large Language Models Strong understanding of LLM architectures, inference mechanisms, and foundation models. Hands-on experience designing and deploying production-grade RAG systems. Experience with prompt engineering, prompt optimization, and contextual retrieval. Knowledge of LoRA, QLoRA, PEFT, and model fine-tuning techniques. Experience implementing hallucination reduction and factuality improvement strategies. Familiarity with AI Agents, autonomous workflows, and agent orchestration frameworks. Embeddings & Retrieval Semantic embedding models (OpenAI, Sentence-BERT, Hugging Face, etc.). Document chunking strategies and metadata management. Vector similarity search techniques including cosine similarity and dot-product search. Hands-on experience with vector databases such as Pinecone, FAISS, OpenSearch, Milvus, Qdrant, or Chroma. AI Frameworks & Technologies LangChain LlamaIndex Hugging Face PyTorch TensorFlow FastAPI Haystack or equivalent AI frameworks Cloud & Infrastructure AWS (Bedrock, SageMaker, Lambda, API Gateway, S3, DynamoDB, IAM, KMS, VPC, CloudTrail) Azure and/or GCP experience is preferred. Docker and Kubernetes Enterprise cloud architecture and deployment experience Programming & Software Engineering Strong Python programming skills Experience with Java or C++ is a plus Knowledge of microservices architecture Experience with asynchronous processing, caching, and scalable APIs MLOps & Production AI Model monitoring and observability CI/CD for AI and ML systems Drift detection and performance evaluation Model versioning and rollback strategies Cost and infrastructure optimization Qualifications 7+ years in software/ML engineering, with 2+ years in GenAI/LLMs Proven experience designing and deploying production-grade AI applications and platforms. Show more Show less