About the role
Responsibilities:
- Design and develop autonomous AI agents using modern Agentic AI frameworks.
- Build and optimise Retrieval-Augmented Generation (RAG) pipelines for enterprise use cases.
- Develop LLM-powered applications using APIs from OpenAI, Anthropic, Google Gemini, Azure OpenAI, or open-source models.
- Implement prompt engineering strategies to improve response quality and reliability.
- Build multi-agent workflows for complex task orchestration.
- Integrate AI agents with enterprise systems, APIs, databases, and business workflows.
- Evaluate AI models using automated evaluation frameworks and establish quality benchmarks.
- Develop scalable backend services for AI applications using Python and FastAPI.
- Implement observability, monitoring, logging, and performance optimisation for AI systems.
- Work closely with product managers and engineering teams to translate business problems into AI-driven solutions.
- Stay updated with emerging advancements in LLMs, Agentic AI, and Generative AI technologies.
Requirements:
- 3.6-5 years of software development experience with strong expertise in Python.
- Hands-on experience with Agentic AI frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or similar.
- Strong understanding of Large Language Models (LLMs) and Generative AI concepts.
- Experience building RAG applications using vector databases.
- Experience with vector databases such as Pinecone, Weaviate, ChromaDB, FAISS, or Milvus.
- Knowledge of embeddings, semantic search, chunking, and retrieval optimisation.
- Strong prompt engineering skills.
- Experience with FastAPI or similar backend frameworks.
- Good understanding of REST APIs and microservices architecture.
- Experience with Git and modern software development practices.
- Familiarity with Docker and containerised deployments.
Good-to-Have Skills:
- Experience with Model Context Protocol (MCP).
- Knowledge of AI evaluation frameworks such as DeepEval, Ragas, LangSmith, or Promptfoo.
- Experience with LlamaIndex.
- Familiarity with Hugging Face ecosystem and open-source LLMs.
- Experience with cloud platforms such as GCP, AWS, or Azure.
- Understanding of Kubernetes and CI/CD pipelines.
- Experience with monitoring tools such as Langfuse, Phoenix, or MLflow.
- Knowledge of AI safety, guardrails, and responsible AI practices.
Preferred Qualifications:
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Information Technology, or a related field.
- Strong problem-solving and analytical skills.
- Excellent communication and collaboration abilities.
- Passion for emerging AI technologies and continuous learning.