Role Summary :

Design and ship Python backends that power AI-driven applications. Youll partner with AI engineers (LLM/RAG/agent teams) to turn prototypes into secure, scalable, observable servicesAPIs, workers, and event pipelinesthat integrate with enterprise systems such as SAP, Salesforce, and ServiceNow.

Responsibilities :

- Build and maintain REST/gRPC APIs with FastAPI (or Flask + Pydantic), using asyncio for I/O-heavy paths.

- Implement background jobs & schedulers (Celery/RQ/Arq) and event pipelines with Amazon SQS/SNS.

- Model data with SQLAlchemy (2.x) + Alembic; performance tuning for Amazon RDS (SQL Server); caching with ElastiCache (Redis).

- Wrap AI components (LLM endpoints, tool/function calling, RAG services) behind stable interfaces; handle streaming, retries, and timeouts.

- Integrate with enterprise applications (SAP/Salesforce/ServiceNow/Workday) using OAuth2/OIDC, robust error handling, and idempotency keys.

- Enforce security & compliance using Okta for IAM, AWS Secrets Manager for credential storage, and structured input validation (JSON Schema).

- Build APIs and services that integrate with AWS Bedrock models, custom RAG services, and OpenSearch Serverless vector stores.

- Ensure backend observability through OpenTelemetry and Datadog, including metrics, tracing, and logging.

- Own operability aspects-feature flags, blue-green/canary release support, incident response workflows, and crisp documentation.

Must-Have Experience :

- 3-5 years backend development in Python.

- FastAPI (preferred) or Flask with Pydantic models; OpenAPI/Swagger API design.

- Async programming (async/await), concurrency patterns, connection pooling, and backpressure management.

- Database expertise with Amazon RDS (SQL Server/PostgreSQL) and Redis; schema design, indexing, query optimization; migrations with Alembic.

- Event-driven architecture experience using Amazon SQS and Amazon SNS.

- API security and integration design with OAuth2/OIDC, JWT, and rate limiting.

- Containerized application development with Docker (deployment managed by platform team on Amazon EKS).

- Testing: pytest, fixtures, mocks/stubs, contract tests, and load testing (k6/JMeter).

- Observability: OpenTelemetry, Datadog, structured logging, and actionable alerts.

- Solid understanding of secrets and identity management with AWS Secrets Manager and Okta.

Nice to Have :

- Built streaming chat endpoints (SSE/WebSockets) and function/tool-calling adapters for AI services.

- Worked with OpenSearch Serverless, Bedrock Knowledge Base, or other vector databases for RAG workflows.

- Experience integrating backend systems with AWS Bedrock models and NeMo Guardrails for runtime safety.

- Exposure to Kong API Gateway, feature flags (LaunchDarkly/Flipt), or policy-as-code (OPA).

- Multi-tenant controls (RBAC, quotas, usage metering) and enterprise-grade integration patterns.

- Familiarity with enterprise APIs (SAP OData/BAPI, Salesforce REST/Graph, ServiceNow).

- Tech Stack (our core; equivalents welcome)

- Python 3.11+, FastAPI, Pydantic v2, SQLAlchemy 2.x, Alembic, pytest.

- Amazon SQS/SNS for messaging and events.

- Amazon RDS (SQL Server/PostgreSQL) for relational data, ElastiCache (Redis) for caching.

- AWS Bedrock for model hosting and RAG services.

- Amazon S3 for data storage, Amazon ECR for container images.

- OpenSearch Serverless for vector storage and search.

- AWS Secrets Manager, Okta IAM for authentication and secrets.

- NeMo Guardrails on Amazon EKS for runtime safety.

- Datadog + OpenTelemetry for observability and monitoring.

- Kong API Gateway for service routing and access control.

- Custom RAG Service and LLM evaluation using Phoenix/Arize/Promptfoo.