The Role
Lead backend engineer responsible for designing and maintaining scalable Python services, APIs, event-driven AWS systems, data layers, RAG pipelines, and production AI agents. The role covers distributed systems, authentication, observability, infrastructure automation, dbt and Airflow data-platform workflows, reliability, security, testing, and CI/CD. The engineer will set technical direction, design service and API architectures, conduct code reviews, mentor teammates, and optimize AI quality, performance, cost, and security.
Summary Generated by Built In
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.
- 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.
- 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.
- Production AWS: ECS/containers, Lambda, IAM, API Gateway, DynamoDB.
- Infrastructure as code with Terraform; CI/CD (GitHub Actions or equivalent) and container builds.
- Building services that are horizontally scalable, resilient, and loosely coupled; handling consistency, retries, idempotency, and partial failure.
- OAuth/OIDC, authn/authz, token handling, least-privilege access, multi-tenant isolation, secrets management.
- 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.
- 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.
Skills Required
- 5+ years designing, building, and maintaining production backend services at scale
- 10+ years of engineering experience for Lead-level candidates
- Expert-level Python for server-side development
- Experience with a web or asynchronous framework such as aiohttp, FastAPI, or Flask
- Experience designing REST or gRPC APIs, streaming patterns, pagination, versioning, idempotency, and backward-compatible contracts
- SQL, data modeling, query optimization, indexing, transactions, and connection pooling
- Experience with relational, warehouse, and NoSQL or key-value data stores
- Knowledge of caching, background jobs, queues, event-driven processing, rate limiting, retries, backoff, and timeouts
- Performance and reliability engineering experience, including profiling, load handling, graceful degradation, and failure design
- Observability experience with structured logging, metrics, distributed tracing, and production debugging
- Object-oriented programming, SOLID principles, design patterns, and domain modeling
- Strong data structures and algorithms knowledge
- Concurrency and asynchronous programming experience
- Unit, integration, and end-to-end testing experience
- Git and pull-request-based development workflows
- Production AWS experience with ECS, containers, Lambda, IAM, API Gateway, and DynamoDB
- Infrastructure-as-code experience with Terraform
- CI/CD and container build experience
- Experience building horizontally scalable, resilient, and loosely coupled distributed services
- OAuth/OIDC, authentication, authorization, token handling, least-privilege access, multi-tenant isolation, and secrets management
- Production LLM application development experience
- RAG experience including chunking, embeddings, vector search, hybrid search, reranking, grounding, citations, and retrieval evaluation
- AI agent experience including prompt engineering, tool use, function calling, structured outputs, and agent orchestration
- Experience integrating Anthropic/Claude, AWS Bedrock/AgentCore, or Snowflake Cortex
- AI evaluation, guardrails, tracing, observability, and token, latency, and cost optimization experience
- AI security experience including prompt injection, data exfiltration, PII handling, and identity or RBAC enforcement
- Experience with dbt
- Experience with Airflow or MWAA
- Experience with Snowflake
- Experience with MCP or RAG
- Slack platform experience, including Bolt, Socket Mode, or Block Kit
- Experience with fine-tuning, adaptation, semantic caching, model routing, or fallback
- Experience adding AI capabilities to existing production systems
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The Company
What We Do
Relanto is a global advisory, consulting, and technology-services partner that helps organizations navigate business transformation as artificial intelligence reshapes the business landscape. Founded in 2021, the company combines advisory expertise with consulting and technology services, operating across a global footprint that includes India and the United States. Its mission centers on applying AI and modern technology to evolving enterprise needs.
