Client is seeking a Lead AI Developer to join our cutting-edge AI initiative in the healthcare industry. This role is ideal for a hands-on technologist with deep expertise in AWS Bedrock, Generative AI, Large Language Models (LLMs), AI-powered search, and Agentic AI workflows. You'll take ownership of designing and deploying scalable, real-world AI applications with a strong focus on Retrieval-Augmented Generation (RAG), vector databases, and cloud-native deployment.
Key Responsibilities:
- Design, build, and deploy scalable AI-powered applications using Amazon Bedrock, OpenAI, or Hugging Face models.
- Implement Retrieval-Augmented Generation (RAG) pipelines for intelligent document understanding and summarization.
- Build and optimize Agentic AI workflows for complex decision-making and task automation.
- Extract and vectorize data from unstructured sources to improve model performance and retrieval accuracy.
- Work with vector databases such as OpenSearch, FAISS, or Pinecone to enable low-latency AI search capabilities.
- Deploy APIs using AWS ECS (preferred), Lambda, and other AWS cloud-native tools with a focus on scalability and cost efficiency.
- Tune AI model performance with fine-tuning, caching strategies, and inference optimization.
- Lead Proof-of-Concept (PoC) efforts for rapid prototyping and business validation.
- Collaborate with data engineers, ML engineers, and cloud architects to drive innovation and excellence in delivery.
Required Skills & Experience:
- 10 to 15 years of total experience in software engineering with a recent focus on AI and cloud-native development.
- Hands-on experience with LLMs, generative AI models, and AI search systems.
- Strong knowledge of Amazon Bedrock or similar platforms like OpenAI or Hugging Face.
- Deep familiarity with RAG pipelines, vector stores, and knowledge retrieval techniques.
- Proven experience in building Agentic AI workflows for autonomous decision-making and task orchestration.
- Strong coding skills in Python or similar languages commonly used in AI development.
- Experience deploying APIs and services via AWS ECS, Lambda, and other serverless technologies.
- A mindset for experimentation, iteration, and performance optimization.
- Prior experience in healthcare or insurance data workflows.
- Knowledge of Kubernetes, Docker, or other container orchestration tools (even though ECS is preferred).
- Familiarity with AWS cost optimization techniques for AI workloads.
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