Source description
About the role
- Architecture & System Design
- Contribute to the design of scalable, multi-agent AI architectures for data discovery and query generation
- Design components and modules across agent orchestration, tool systems, and LLM integration
- Evaluate trade-offs across design choices (e.g., single vs multi-agent, RAG vs fine-tuning, deterministic vs probabilistic pipelines)
- Participate in design reviews and contribute to architecture decision records (ADRs)
- Hands-On Engineering & Execution
- Write production-grade code across agent frameworks, backend APIs, and frontend interfaces daily
- Build and evolve reusable AI components (agent tools, embedding pipelines, evaluation frameworks)
- Implement LLM-powered workflows including NL-to-SQL generation, semantic search, and metadata enrichment
- Develop services enabling intelligent data access (vector search, hybrid retrieval, query scope management)
- Implement guardrails, validation layers, and observability for AI-generated outputs
- Full Stack Development
- Build performant backend services (Python/FastAPI) and interactive frontends (Angular/React) for data exploration
- Develop both conversational (chat) and structured (API) interfaces for analytics
- Build evaluation and benchmarking tooling for continuous AI quality measurement
- Own features end-to-end from design through deployment and monitoring
- Semantic Search & Embeddings
- Implement vector embedding pipelines for metadata discovery (pgvector)
- Build semantic retrieval across datasets, tables, and columns with hybrid search strategies
- Optimize search relevance through embedding strategies, re-ranking, and evaluation metrics
- Contribute to data quality and governance capabilities within the platform
- Engineering Excellence
- Write clean, maintainable, and scalable code following best practices (SOLID, DRY, design patterns)
- Actively participate in code reviews and set quality standards through your own contributions
- Perform root cause analysis on agent failures and implement systematic fixes
- Anchor the team technically — be the go-to person for complex implementation challenges
- Collaboration
- Partner with Product, Data Engineering, and Platform teams on feature delivery
- Support teammates through pair programming, knowledge sharing, and technical guidance
- Contribute to sprint planning, estimation, and technical feasibility assessments
- Help onboard new team members and share domain expertise
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