GenAI / LLM Application Engineer

Role Summary :

We are looking for a GenAI / LLM Application Engineer to build production-grade AI assistants, retrieval systems, and workflow automation solutions. The role requires hands-on experience with multi-agent orchestration, RAG pipelines, search and retrieval infrastructure, backend APIs, and user-facing chatbot experiences.

The ideal candidate can take a business workflow, break it into AI-enabled subtasks, connect the right tools and services, and deliver reliable, measurable outcomes.

Key Responsibilities :

- Design and implement multi-agent orchestration systems that can decompose complex user requests into smaller tasks.

- Build retrieval-augmented generation pipelines for enterprise documents and knowledge bases.

- Develop and optimize search flows using vector search, semantic search, keyword search, and hybrid retrieval.

- Integrate LLMs with external tools, APIs, and enterprise systems such as ticketing, HR, or support platforms.

- Create backend services using Python and FastAPI to power AI features and APIs.

- Work with LangChain and LangGraph to implement agent workflows, tool calling, and retrieval logic.

- Build and maintain chatbot interfaces using React, Chainlit, or similar frontend frameworks.

- Manage token limits, prompt quality, context handling, and conversation history strategies.

- Evaluate response quality using human review, recall vs precision analysis, and automated evaluation methods.

- Collaborate with product, backend, and business teams to translate workflows into AI solutions.

Required Skills :

- Strong experience in Python backend development.

- Hands-on experience with FastAPI or similar API frameworks.

- Practical knowledge of LangChain and/or LangGraph.

- Experience building RAG systems with chunking, embeddings, retrieval, and prompt assembly.

- Familiarity with Elasticsearch or other vector/search databases.

- Understanding of embeddings, transformers, attention, tokenization, and generation settings such as temperature, top-p, and top-k.

- Ability to design prompt and context management strategies for longer conversations.

- Experience integrating third-party APIs and enterprise tools.

- Strong problem-solving ability and production-oriented engineering mindset.

Preferred Skills :

- Experience with MCP and tool-based agent architectures.

- Knowledge of reranking, hybrid search, BM25, and HNSW.

- Experience with Azure AI Search, Azure OpenAI, blob storage, or other Azure services.

- Experience with React or Chainlit for AI chatbot interfaces.

- Exposure to automated evaluation frameworks such as Ragas.

- Familiarity with distributed systems, event-driven workflows, or microservices.

Qualifications :

- 5+ years of software engineering experience, with at least part of that focused on AI or backend development.

- Proven ability to ship features end to end, from backend logic to user-facing experience.

- Experience working in production environments with measurable performance or quality outcomes.