AI Backend Software Developer

We are seeking a seasoned Generative AI Backend Engineer to join our team.

This is an opportunity for a skilled backend engineer to own the development of core AI modules, including Model Context Protocol (MCP) servers, Retrieval-Augmented Generation (RAG) frameworks, and Agent-to-Agent (A2A) orchestration systems.

The successful candidate will have hands-on experience with Python engineering, GenAI capabilities like tool orchestration, retrieval pipelines, and observability. They will design, implement, and optimize AI backends that combine Python engineering discipline with GenAI capabilities.

A key responsibility of this role is to architect, implement, and optimize AI backends that combine Python engineering discipline with GenAI capabilities. The engineer will work on end-to-end involvement from architecture to MVP rollout.

In addition to technical skills, the ideal candidate should have excellent communication and collaboration skills. They should be able to work effectively in a fast-paced environment and prioritize multiple tasks simultaneously.

Key Responsibilities:

• Design, implement, and optimize AI backends that combine Python engineering discipline with GenAI capabilities.
• Architect and implement MCP servers, RAG frameworks, and A2A orchestration systems.
• Develop APIs, microservices, and data pipelines to support AI use cases.
• Work with cloud infrastructure (AWS/GCP/Azure) and deploy solutions using Docker, Kubernetes, and cloud platforms.
• Implement observability, replay, and guardrails to A2A interactions.

Required Skills & Qualifications:

• 3–7 years professional experience with Python (3.9+).
• Strong knowledge of OOP, async programming, and REST API design.
• Hands-on experience with RAG implementations and vector databases (Pinecone, Weaviate, FAISS, Qdrant, Milvus).
• Familiarity with MCP (Model Context Protocol) 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 LLMs.
• 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.

Benefits:

• End-to-end involvement from architecture → MVP → production rollout.
• A fast-moving, engineering-first culture where experimentation is encouraged.
• Competitive compensation, flexible work setup, and strong career growth.

Nice-to-Have:

• Exposure to AI observability & evaluation (LangSmith, PromptFoo, Ragas).
• Contributions to open-source AI/ML or MCP projects.
• Understanding of compliance/security frameworks (SOC-2, GDPR, HIPAA).
• Prior work with custom embeddings, fine-tuning, or LLMOps stacks.