Were looking for a Full-Stack Developer to join our R D AI/ CTO /Innovation team and help build internal and customer-facing AI products and platforms. Youll work across backend services, data stores, and modern web UIs, especially around LLM-enabled applications (RAG, agentic workflows, and emerging interoperability patterns).
Responsibilities
Design, build, and maintain backend services in Python (e.g., FastAPI) running on Linux and containerized with Docker
Develop and integrate APIs for AI/LLM-driven features (e.g., retrieval, orchestration, evaluation, safety/guardrails as needed)
Implement RAG pipelines: ingestion, chunking, embedding, indexing, retrieval, re-ranking, prompt construction, and response generation
Work with multiple database paradigms depending on the use case:
Relational
Document stores
Vector databases (semantic search, hybrid search)
Graph databases (knowledge graphs, relationships, reasoning support)
Build and maintain React frontends for AI experiences (chat/agent UIs, dashboards, tooling)
Collaborate with researchers, product/innovation stakeholders, and platform/infra teams to turn prototypes into robust services
Write tests, improve Developer experience, and contribute to architecture decisions, code reviews, and documentation
Requirements: Required qualifications
Strong experience as a Full-Stack Developer, with production work in:
Python backend (FastAPI or similar)
Modern frontend development in React
Hands-on experience with Linux environments and Docker-based workflows
Solid understanding of API design (REST; event-driven is a plus), authentication/authorization basics, and service reliability fundamentals
Practical experience with following database types: SQL, document, vector, graph
Familiarity with LLM application development, including:
RAG concepts and trade-offs
Token/context constraints, latency/cost considerations
Evaluation/quality approaches (offline + online)
Nice-to-have skills
Understanding of MCP and A2A concepts/patterns (or experience with agent frameworks and tool-use protocols)
Experience with embeddings, semantic search, hybrid retrieval, and reranking approaches
Experience deploying services (CI/CD, observability, monitoring, logging, tracing)
Familiarity with Kubernetes, cloud platforms, or infrastructure-as-code
Security-minded development (secrets management, least privilege, threat modeling for LLM apps)
UX for AI products (chat interaction patterns, streaming responses, citations, feedback loops)
What success looks like
You can independently deliver end-to-end features from backend to React UI
You build AI-enabled services that are reliable, testable, and measurable
You can choose appropriate Storage /retrieval approaches (SQL vs doc vs vector vs graph) based on the problem
You improve iteration speed for the R D team (tooling, templates, reusable components)
This position is open to all candidates.
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