About the role
We are looking for an AI Engineer who has hands-on experience in agentic AI, RAG pipelines, LLM orchestration, and scalable backend systems to help build next-generation AI products.
Responsibilities:
- Design and build Agentic AI applications using LLMs, tools, and autonomous workflows.
- Develop and optimize RAG (Retrieval Augmented Generation) pipelines for enterprise-scale knowledge retrieval.
- Integrate vector databases, document ingestion systems, and observability frameworks.
- Work on prompt engineering, context management, evaluation, and model optimization.
- Build AI-powered products leveraging OpenAI/Anthropic/Gemini and open-source LLMs.
- Collaborate with product, engineering, and data teams to deliver production-ready AI solutions.
- Drive improvements in latency, accuracy, scalability, and AI infrastructure costs.
Requirements:
- Engineers who have shipped AI features/products to production.
- Strong problem-solving and system design skills.
- Ability to work in a fast-paced product environment.
- Passion for building innovative AI-first experiences.
- 3-7 years of software engineering experience.
- Strong proficiency in Python and/or Golang.
- Hands-on experience with agentic AI frameworks, LangChain / LangGraph, RAG architecture, prompt engineering, LLM evaluation and optimization, vector databases (Pinecone, Weaviate, Chroma, etc. ), OpenAI, Anthropic, Gemini APIs, document processing, and knowledge retrieval.
- Experience with PostgreSQL / NoSQL databases, REST APIs and microservices, cloud platforms (AWS / GCP / Azure), Docker, and Kubernetes.
- Preferred Skills: AI observability tools (Langfuse, LangSmith, Helicone), multi-agent systems, fine-tuning and model customization, MCP (Model Context Protocol), and AI workflow automation.
- Experience building enterprise AI products from scratch.