Gen AI Developer (Java + MCP)

Gen AI Developer (Java + MCP) -

Total Experience 5 to 10 Years

Top 3 Must Haves:

1. Java

2. Generative AI + LLM

3. MCP/RAG

4. Clouds like Azure/AWS

JD:

The customer is looking for a Software Engineer who can contribute to the design and development of AI-driven applications using Java, and modern Gen AI frameworks. You’ll work on prompt engineering, Retrieval-Augmented Generation (RAG), agentic workflows, and scalable backend systems that integrate with LLMs and enterprise data using the Model Context Protocol (MCP).

Key Responsibilities:

• Develop and maintain backend services and AI agents using Java.

• Build and optimize Gen AI applications using:

• Prompt engineering

• Retrieval-Augmented Generation (RAG)

• Agentic flows (multi-agent orchestration, tool use, memory)

• Implement Model Context Protocol (MCP) to standardize AI interactions with external tools, databases, and services.

• Knowledge with vector databases (e.g., FAISS, Pinecone, Weaviate) for semantic search and context retrieval.

• Collaborate with product and research teams to translate AI roadmap goals into technical deliverables.

Required Skills:

• Strong programming skills in Java

• Solid understanding of Generative AI concepts, including:

• Prompt design and tuning

• RAG pipelines and document chunking

• Agentic workflows (e.g., LangChain agents, tool calling)

• Familiarity with LLM APIs (OpenAI, Anthropic, etc.) and open-source models e.g., LLaMA, Mistral.

• Experience implementing MCP for scalable, secure, and standardized AI-to-tool integrations.

• Knowledge of RESTful APIs, microservices, and cloud platforms (AWS, Azure, GCP).

Nice to Have:

• Experience with Gen AI frameworks like LangChain, LlamaIndex, or Haystack.
• Exposure to embedding models, tokenization, and context window optimization.
• Understanding of MCP architecture: clients, servers, and host applications.
• Prior work in AI/ML projects, NLP, or conversational AI.
• Knowledge of CI/CD pipelines, containerization (Docker, Kubernetes), and observability tools.
• Knowledge on integrating LLMs (e.g., OpenAI, Anthropic, Mistral) with internal systems and APIs.

Relevant Gen AI Topics You’ll Work With:

• Prompt engineering best practices (zero-shot, few-shot, chain-of-thought)
• Retrieval-Augmented Generation (RAG) with hybrid search
• Agentic architectures (e.g., autonomous agents, task decomposition)
• Model Context Protocol (MCP) for tool and data integration
• Evaluation and safety of Gen AI outputs
• Fine-tuning and model selection strategies responsibilities

Preferred candidate profile