Location: [Remote / Onsite – USA]
Employment Type: [Full-Time / Contract]
We are seeking a highly skilled AI / LLM Engineer with expertise in Retrieval-Augmented Generation (RAG) to design, develop, and deploy intelligent AI systems. The ideal candidate will have hands-on experience with Large Language Models (LLMs), vector databases, and building scalable AI-driven applications that integrate external knowledge sources.
Design and implement RAG-based architectures to enhance LLM performance with real-time and domain-specific data.
Develop and optimize LLM-powered applications using frameworks like LangChain, LlamaIndex, or similar.
Build and manage vector databases (e.g., Pinecone, FAISS, Weaviate, Chroma).
Integrate APIs, data pipelines, and external knowledge sources into AI systems.
Fine-tune and evaluate LLMs for accuracy, latency, and cost efficiency.
Implement prompt engineering strategies and optimize chain-of-thought reasoning.
Ensure scalability, reliability, and performance of AI applications.
Work closely with cross-functional teams to deliver production-grade AI solutions.
Monitor and improve model performance, hallucination reduction, and response quality.
Implement security, governance, and compliance best practices in AI systems.
Strong experience with LLMs (GPT, LLaMA, Claude, etc.)
Hands-on experience with RAG architecture and implementation
Proficiency in Python and AI/ML frameworks
Experience with LangChain, LlamaIndex, or similar orchestration tools
Knowledge of vector databases (Pinecone, FAISS, Weaviate, etc.)
Familiarity with cloud platforms (AWS, Azure, GCP)
Experience with REST APIs, microservices, and backend development
Understanding of NLP, embeddings, and semantic search
Strong problem-solving and system design skills
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11+ years of experience designing and deploying enterprise-grade AI solutions across healthcare and financial domains, with specialization in Agentic AI systems (LangChain, AutoGen, LangGraph) and advanced LLM fine-tuning (LoRA, PEFT) with RAG.
Proven expertise in Python, TensorFlow, PyTorch, Hugging Face, and MLOps pipelines (MLflow, Kubeflow), alongside NLP, data engineering (BigQuery, Dataflow, Cosmos DB), and secure, scalable deployments via Kubernetes and AKS.
Delivered GenAI-powered clinical decision support systems on Vertex AI for automated EHR summarization, disease prediction, and HIPAA-compliant deployment in high-regulation environments (HIPAA, PCI-DSS, GDPR).
Strong track record mentoring cross-functional teams on AI model development, MLOps best practices, and domain-specific AI implementations in enterprise settings.
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