Lead Backend Engineer - Data Engineering & AI

Design, build, and maintain the backend services, APIs, data-platform automation, and AI agents behind our data products. This is a backend-heavy role: distributed Python services, streaming LLM/agent runtimes on AWS, RAG pipelines, and the dbt/Airflow automation that powers our data products.

Responsibilities

• Design, build, and maintain backend services and APIs in Python — RESTful and streaming (SSE) endpoints, agent runtimes on AWS Bedrock AgentCore / ECS Fargate, and event-driven Lambda handlers.
• Architect service boundaries and data flows: define contracts between services, model persistence, and manage state, caching, and asynchronous/background processing.
• Build RAG pipelines and tool-calling AI agents over our data products: retrieval, orchestration, grounding, and evaluation.
• Design data access and storage layers: schema/data modeling, query performance, connection/session management, and integration with warehouses (Snowflake) and key-value stores (DynamoDB).
• Implement auth and identity: OAuth/OIDC flows (per-user 3LO, token vaulting, session binding), least-privilege IAM, secrets management.
• Build and maintain data-platform automation: dbt models, MWAA/Airflow orchestration, and tooling for discoverable, governed, consumable data products.
• Own service reliability and delivery: Terraform, GitHub Actions CI/CD, container builds, structured logging, metrics/tracing, alerting, and cost controls.
• Set technical direction: system and API design, code review, and mentoring.

Required skills

Backend engineering

• 5+ years designing, building, and maintaining production backend services at scale. 10+ years if Lead level engineering candidate.
• Expert-level Python for server-side development; solid grasp of at least one web/async framework (e.g. aiohttp, FastAPI, Flask) and the WSGI/ASGI model.
• Service and API design: REST (and/or gRPC), request/response and streaming patterns, pagination, versioning, idempotency, and backward-compatible contracts.
• Data layer: SQL and data modeling, query optimization and indexing, transactions, connection pooling; experience with relational, warehouse (Snowflake), and NoSQL/key-value (DynamoDB) stores.
• Server-side patterns: caching strategies, background jobs/workers, queues and event-driven processing, rate limiting, retries/backoff, and timeouts.
• Performance & reliability: profiling, load handling, latency/throughput trade-offs, graceful degradation, and designing for failure.
• Observability: structured logging, metrics, distributed tracing, and debugging live production issues.

Software engineering fundamentals

• Object-oriented programming (required): encapsulation, abstraction, inheritance, composition, polymorphism; SOLID principles; design patterns applied pragmatically; strong domain modeling.
• Solid data structures & algorithms; ability to reason about time/space complexity.
• Concurrency and async programming (async/await, threading, event loops) and their failure modes.
• Testing (unit, integration, end-to-end) and testable design; Git and PR-based workflows; disciplined code review.

Cloud & infrastructure

• Production AWS: ECS/containers, Lambda, IAM, API Gateway, DynamoDB.
• Infrastructure as code with Terraform; CI/CD (GitHub Actions or equivalent) and container builds.

Distributed systems

• Building services that are horizontally scalable, resilient, and loosely coupled; handling consistency, retries, idempotency, and partial failure.

Security

• OAuth/OIDC, authn/authz, token handling, least-privilege access, multi-tenant isolation, secrets management.

AI / LLM engineering

• Building LLM applications in production (not research).
• RAG: chunking, embeddings, vector search, hybrid search, reranking, grounding/citations, context-window management, retrieval evaluation.
• Agents: prompt engineering, tool use/function calling, structured outputs, agent orchestration (single- and multi-step), prompt caching.
• Integration with model providers — Anthropic/Claude, AWS Bedrock/AgentCore, Snowflake Cortex — and response streaming.
• AI quality & ops: eval harnesses, guardrails, tracing/observability, token/latency/cost optimization.
• AI security: prompt injection, data exfiltration, PII handling, per-user identity/RBAC enforcement.

Nice to have

• dbt, Airflow/MWAA, Snowflake.
• MCP (Model Context Protocol) and/or RAG
• Slack platform (Bolt, Socket Mode, Block Kit) or other real-time/conversational backends.
• Fine-tuning/adaptation, semantic caching, or model routing/fallback.
• Experience adding AI capabilities to existing production systems.

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