Must have

Expertise in generative AI frameworks and multi?agent systems (AutoGen, CrewAI, Dify) combined with RAG pipelines built on LangChain, LlamaIndex, and vector databases (Pinecone, FAISS) to enable contextual AI applications.
Full?stack development proficiency with Java Spring Boot, React/Angular, and RESTful API design, enabling seamless integration of AI services into enterprise applications.
Cloud?native AI deployment experience on Google Cloud (Vertex AI) and AWS (Bedrock, SageMaker) with secure API management using Apigee X and CI/CD automation via GitHub, Jenkins, Docker, and Kubernetes

Good to Have

Familiarity with AI?assisted development tools such as GitHub Copilot, Gemini Code Assist, Amazon Q, and Cursor to boost developer productivity and code quality.
Domain expertise in P&C insurance, healthcare automation, and e?commerce product catalog/search, allowing rapid translation of business needs into AI solutions.
Experience leading innovation programs, internal hackathons, and design sprints that foster adoption of emerging AI technologies across the enterprise.

Roles & Responsibilities

Serve as the solution architect for AI/ML initiatives, designing end?to?end architectures that integrate generative AI models, multi?agent frameworks (AutoGen, CrewAI, Dify) and micro?service back?ends built with Java Spring Boot and React/Angular.
Lead rapid prototyping and innovation labs, orchestrating internal hackathons and proof?of?concepts that leverage AI copilots (GitHub Copilot, Gemini, Amazon Q, Cursor) and prompt engineering to accelerate feature delivery.
Translate business objectives from domains such as P&C insurance, healthcare, and e?commerce into scalable AI solutions, employing RAG pipelines with LangChain, LlamaIndex, and vector databases (Pinecone, FAISS, Weaviate) for contextual retrieval.
Oversee the full AI delivery pipeline-from data ingestion and preprocessing using ETL and vectorization, through model training on LLMs (Hugging Face, Vertex AI, AWS Bedrock) to CI/CD deployment with GitHub, Jenkins, Docker and Kubernetes.
Implement and govern model lifecycle management, including monitoring, versioning, and automated testing using MLOps tools like MLflow, Kubeflow, and Vertex AI Pipelines, ensuring compliance with security and governance policies.
Secure APIs and services using Apigee X (OAuth 2.0, TLS) and enforce enterprise?wide standards for data privacy, especially for healthcare discharge?summary generators and insurance policy automation.
Mentor and provide technical leadership to cross?functional engineering and data?science teams, conducting code reviews, prompt?engineering workshops, and knowledge?transfer sessions on generative AI best practices.
Drive integration of AI?assisted development tools (GitHub Copilot, Gemini Code Assist, Cursor) into the software development lifecycle to improve developer productivity and code quality.
Collaborate with product owners, compliance officers, and infrastructure teams to ensure AI solutions are scalable, highly available, and meet regulatory requirements across cloud platforms (Google Cloud, AWS).
Contribute to pre?sales and solutioning activities, creating technical proposals, demos, and architecture diagrams that showcase AI?first capabilities for enterprise clients.

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