Platform Software Engineer

We are looking for a hands-on Tech Anchor / Senior Software Engineer to drive the design and implementation of core AI capabilities within our Intelligent Data Analytics Platform (Lightspeed), spanning multi-agent orchestration, natural language-to-SQL generation, and semantic data discovery. This role is for a strong individual contributor who can operate at both architectural and implementation depth, someone who anchors the team technically by writing production-grade code, solving the hardest problems, and setting engineering standards through example.

You will be a key contributor to an AI-first platform that enables users to explore, query, and analyze enterprise BigQuery data through natural language reliably, accurately, and at scale.

Responsibilities 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 as 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.

What We're Looking For

• 6+ years of professional software engineering with strong hands-on coding ability.
• Experience building AI-powered applications or working with LLM-based systems in production.
• Ability to take ambiguous requirements and deliver working, tested software independently.
• Strong debugging and problem-solving skills across the full stack.
• Track record of owning and delivering complex features end-to-end.

Technology Stack

• Programming: Python (primary), Java, TypeScript, Angular/React
• AI/ML: Google ADK, LangChain/LangGraph, OpenAI/Gemini APIs, prompt engineering, RAG pipelines
• Data & Cloud: GCP (BigQuery, Vertex AI, Cloud Run preferred)
• Backend: FastAPI, Pydantic, SQLModel/SQLAlchemy, PostgreSQL (pgvector)
• Frontend: Angular or React, TypeScript
• CI/CD & Infra: Terraform, GitHub Actions, Docker
• Evaluation: Custom eval frameworks, LLM-as-judge patterns
• Tools: Git, Alembic

Nice to Have

• Experience with Google Agent Development Kit (ADK) or similar agent frameworks (AutoGen, CrewAI, LangGraph).
• Exposure to NL-to-SQL or text-to-code generation systems.
• Knowledge of ML fundamentals embeddings, classification, clustering, evaluation metrics.
• Experience with vector databases and semantic retrieval optimization.
• Familiarity with data governance (metadata management, lineage, data quality).
• Experience building developer tooling or platform SDKs.