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.

- 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) 2. 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 3.

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 4.

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 5.

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 6. 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 - 5 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 - Robust 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 Experience Level Senior Level

📌 Senior Platform Software Engineer - AI & Data Analytics (Chennai)
🏢 Ford Motor
📍 Chennai