Backend Engineer – AI

Backend Engineer – AI

Experience:

Location:

Availability:

Overview

Design and ship Python-based backends that power AI-driven applications. You’ll work closely with AI engineers (LLM, RAG, and agent teams) to turn prototypes into secure, scalable, and observable production systems. The role involves building APIs, workers, and event pipelines that integrate seamlessly with enterprise environments.

Key Responsibilities

• API Development:

  Build and maintain REST/gRPC APIs using

  FastAPI

  (preferred) or

  Flask + Pydantic

  , leveraging

  asyncio

  for high-performance I/O.

• Background Jobs & Eventing:

  Implement schedulers and workers (

  Celery/RQ/Arq

  ) and event pipelines (

  Kafka/RabbitMQ/Azure Service Bus

  ).

• Data Modeling:

  Design and tune schemas with

  SQLAlchemy 2.x

  and

  Alembic

  , optimize PostgreSQL performance, and implement caching using

  Redis

  .

• AI Integration:

  Wrap AI components (LLM endpoints, RAG services, tool/function calling) behind stable, observable interfaces with streaming and timeout management.

• Enterprise Integration:

  Connect to enterprise systems (SAP, Salesforce, ServiceNow, Workday) with

  OAuth2/OIDC

  , error handling, and idempotency safeguards.

• Security & Compliance:

  Implement

  identity and access management

  ,

  Key Vault/Secrets Manager

  , input validation (JSON Schema), and maintain audit logs.

• Operational Excellence:

  Own deployment, monitoring, feature flags, blue-green/canary releases, incident response, and post-mortems with clear documentation.

Must-Have Qualifications

• 3–5 years of backend development experience using

  Python

  .
• Expertise in

  FastAPI

  (preferred) or

  Flask + Pydantic

  with a strong understanding of

  OpenAPI/Swagger

  design.
• Solid knowledge of

  async programming

  , concurrency control, and connection management.
• Hands-on experience with

  PostgreSQL

  (schema design, query optimization) and

  Redis

  caching.
• Experience with

  Kafka

  ,

  RabbitMQ

  , or

  Azure Service Bus

  for messaging/eventing.
• Proficiency in

  Docker

  and at least one cloud provider (

  Azure preferred

  ; AWS/GCP acceptable).
• Familiarity with

  CI/CD

  (GitHub Actions or Azure DevOps) and IaC tools (Terraform/Bicep).
• Strong

  testing

  practice: pytest, fixtures, contract/load testing, mocks/stubs.
• Experience with

  observability tools

  (OpenTelemetry, App Insights, Prometheus, Grafana).
• Good understanding of

  security principles

  (OAuth2/OIDC, JWT, mTLS, rate limiting, and input/output validation).

Good-to-Have Skills

• Experience building

  streaming chat endpoints

  (SSE/WebSockets) and function/tool-calling adapters for AI services.
• Exposure to

  vector databases

  (Azure AI Search vector, Pinecone, Weaviate, Qdrant) and

  content-safety

  integrations.
• Knowledge of

  multi-tenant controls

  , policy-as-code (OPA), and usage metering.
• Familiarity with

  gRPC

  ,

  Dapr

  , and feature flag systems (LaunchDarkly, Flipt).
• Enterprise system integration experience (SAP OData/BAPI, Salesforce REST/Graph, ServiceNow).

Core Tech Stack

• Python 3.11+, FastAPI, Pydantic v2, SQLAlchemy 2.x, Alembic, pytest
• PostgreSQL, Redis, Kafka/RabbitMQ/Azure Service Bus
• Docker, GitHub Actions/Azure DevOps, Terraform/Bicep
• OpenTelemetry, App Insights/Prometheus/Grafana
• Auth & Security: OAuth2/OIDC (Entra ID/other), Key Vault/Secrets Manager, API Gateway/WAF