Summary:
We're building AI-powered intelligence workflows that connect multiple enterprise systems and surface real-time insights where teams need them. We're looking for an engineer with solid hands-on experience who can independently design, build, and ship production-grade GenAI solutions - from agentic workflows to end-to-end API integrations - working directly alongside a senior AI architect.
You'll own workstreams, make technical decisions on architecture and integration patterns, and bring full-stack engineering experience that shapes how we build - not just execute tasks handed to you.
Roles & Responsibilities:
- Design, build, and deploy intelligent single and multi-agentic applications using LLMs - task decomposition, planning, and autonomous execution
- Build full-stack AI-powered applications - backend APIs, frontend interfaces, and the integration layer connecting them to enterprise data systems
- Develop and own end-to-end AI-powered workflows integrated with backend systems, APIs, and data pipelines across cloud platforms
- Build and iterate on RAG pipelines - embeddings, vector databases, retrieval logic, and prompt design
- Prototype and ship PoCs using AI agent frameworks (LangChain, LangGraph, LlamaIndex, or equivalent)
- Integrate AI solutions across multiple enterprise platforms and APIs using REST, JSON, and cloud services
- Write clean, production-ready code with documentation, error handling, and maintainability standards
- Translate business requirements into working GenAI solutions independently with minimal hand-holding
- Bring in best practices - CI/CD, testing, observability - and help raise the bar for the team
Required Skills:
- 3–5 years of software engineering or AI engineering experience
- Strong Python proficiency - production code, not just scripts
- Full-stack development experience - backend APIs, frontend, and the integration layer connecting them
- Proven experience building and shipping GenAI or LLM-powered applications end-to-end
- End-to-end API integration experience across multiple systems
- Cloud platform experience - AWS, Azure, or GCP
- Experience with AI agent frameworks - LangChain, LangGraph, LlamaIndex, or equivalent
- Strong debugging and problem-solving mindset - owns failures end-to-end
Good to have Skills:
- Data engineering experience - ETL pipelines, data warehouses (Snowflake, BigQuery, Redshift), or similar
- RAG pipeline experience - embeddings, vector DBs, retrieval and reranking
- Automation or integration platform experience - code or no-code
- Containerization and deployment - Docker, CI/CD pipelines
- MCP (Model Context Protocol) or agentic tool-use patterns
- Experience in a fast-moving startup or consulting environment
