AI Engineer

2 days ago

Kottayam, Kerala, India beBeeGenerative Full time ₹ 20,00,000 - ₹ 25,00,000
AI Engineer - Generative AI Backend

About our project:

We are developing an innovative system utilizing generative AI technology to create robust backend solutions.

This is not a research role—it's about building and deploying production-grade AI systems that work reliably in the wild.

You will design, implement, and optimize AI backends combining software engineering discipline with GenAI capabilities like tool orchestration, retrieval pipelines, and observability.

  • Key Responsibilities

  • Backend Development
  • Generative AI & RAG
  • MCP (Model Context Protocol)
  • Agent-to-Agent (A2A) Workflows
  • Production & Observability
  • Required Skills & Qualifications

  • 3–7 years professional experience with Python (3.9+).
  • Strong knowledge of object-oriented programming, asynchronous programming, and REST API design.
  • Proven hands-on experience with RAG implementations and vector databases (Pinecone, Weaviate, FAISS, Qdrant, Milvus).
  • Familiarity with MCP concepts and hands-on experience with MCP server implementations.
  • Understanding of multi-agent workflows and orchestration libraries (LangGraph, AutoGen, CrewAI).
  • Proficiency with FastAPI/Django for backend development.
  • Comfort with Docker, GitHub Actions, CI/CD pipelines.
  • Practical experience with cloud infrastructure (AWS/GCP/Azure).
  • Add tracing, logging, and evaluation metrics (PromptFoo, LangSmith, Ragas).
  • Optimize for latency, cost, and accuracy in real-world deployments.
  • Deploy solutions using Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).
  • Design and implement multi-agent orchestration (e.g., AutoGen, CrewAI, LangGraph).
  • Build pipelines for agents to delegate tasks, exchange structured context, and collaborate.
  • Add observability, replay, and guardrails to A2A interactions.
  • Develop MCP servers to expose tools, resources, and APIs to large language models.
  • Work with FastMCP SDK and design proper tool/resource decorators.
  • Ensure MCP servers follow best practices for discoverability, schema compliance, and security.
  • Implement RAG pipelines: text preprocessing, embeddings, chunking strategies, retrieval, re-ranking, and evaluation.
  • Integrate with LLM APIs (OpenAI, Anthropic, Gemini, Mistral) and open-source models (Llama, MPT, Falcon).
  • Handle context-window optimization and fallback strategies for production workloads.
  • Build clean, modular, and scalable Python codebases using FastAPI/Django.
  • Implement APIs, microservices, and data pipelines to support AI use cases.

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