Senior Agentic AI Engineer

Coforge; Oaks (PA); Presencial; USD 150.000 - 230.000; Jornada completa

Descripción de la vacante

Coforge in Oaks, PA is seeking a senior backend engineer with 10+ years of software experience to own production-grade services end-to-end.

You will design in Python (async), implement robust APIs, ensure security, observability, and perform rigorous testing while collaborating with frontend, data, and AI tooling teams. The role also requires leadership, mentoring and adherence to enterprise standards.

Formación

• 9+ years in software engineering with 6+ years building and operating backend services in production.
• Python backend development with async, production frameworks, semantic versioning and testing.
• HTTP/API fundamentals: REST, OpenAPI, versioning, error contracts.
• Distributed systems: retries, timeouts, idempotency, tracing across services.
• Identity and access: OAuth 2.0, OpenID Connect, JWT, Azure AD, RBAC.
• LLM/Agent development: production experience with model contexts, tool schemas and memory.
• AI gateways and policy: API gateways and policy engines (Open Policy Agent).
• Data stores: SQL, Cosmos DB, Redis, vector/search stores.
• Observability: OpenTelemetry and structured logging.
• Cloud and delivery: Azure, Docker/Kubernetes, Terraform, CI/CD (Azure DevOps).
• AI-native delivery: using AI coding agents to accelerate development with proper review.
• Communication: clear English, able to explain trade-offs to technical and non-technical audiences.
• Technical writing: design docs, runbooks, decision records.

Conocimientos

Python (Async)

Backend design

HTTP APIs

Distributed systems

OAuth 2.0 / OpenID

LLM / AI agents

Azure / Cloud

Docker / Kubernetes

Observability

Communication

Technical writing

Herramientas

Azure API Management

LangGraph

OpenAI Agents SDK

LangSmith

Docker

Kubernetes

Terraform

Azure DevOps

Descripción del empleo

Experience: 10+ years

Mode of Hire: Full Time

SKILLS REQUIRED:

• Experience: 9+ years in software engineering, including 6+ years building and operating backend services in production, with personal ownership of their design, reliability and security. Experience limited to support, maintenance or configuration work does not meet this requirement.
• Backend engineering in Python: Typed, asynchronous Python with production web frameworks and HTTP clients, package design and publishing with semantic versioning and backward compatibility, pytest with fixtures and mocks, structured logging, configuration handling and command-line tooling. Python is the primary language, and the role calls for working productively in other languages, such as TypeScript, Java or C#, when the work requires it, using AI coding tools to accelerate the transition while holding the same engineering standard.
• HTTP and API fundamentals: HTTP semantics, REST design, OpenAPI, server-sent events and streaming, error contracts, idempotency, pagination and versioning, including calling and debugging APIs from the command line.
• Distributed systems: Retries with backoff, timeouts, circuit breakers, idempotency, consistency and partitioning, queues and events, caching and latency budgets, with the ability to trace a request across gateway, service and store and isolate where it fails.
• Identity and access, applied: OAuth 2.0 client credentials and on-behalf-of flows, OpenID Connect, JWT structure and validation (issuer, audience, signature, claims), Entra ID app registrations and service principals, managed identities and workload identity federation, credential chains in the Azure identity libraries, RBAC and Key Vault, including diagnosis of authentication and authorization failures to root cause.
• Agent and LLM development: Production experience with LLM APIs, including chat completions, tool calling, structured output, streaming, embeddings and token accounting. Hands-on development with one or more agent frameworks, such as LangGraph, the OpenAI Agents SDK, Microsoft Agent Framework or the Claude Agent SDK, including state management, checkpointing and memory stores. Implementation of Model Context Protocol clients and servers, including transports, tool schemas and authentication.
• AI gateways and policy: Working knowledge of AI gateways, such as Azure API Management, LiteLLM, Portkey or Kong, including model aliases, quotas, metering and guardrails. Working knowledge of policy as code with a policy engine such as Open Policy Agent.
• Data stores: SQL and relational design. Document or key-value stores such as Cosmos DB, including partitioning, item size limits, time to live and throughput provisioning. Caches such as Redis. Vector or search stores such as Azure AI Search or pgvector, including hybrid search fundamentals.
• Evaluation and testing: Golden datasets, deterministic assertions and LLM-as-judge rubrics with tools such as promptfoo, DeepEval or RAGAS, evaluation as a pipeline gate, contract and integration test suites, and load testing fundamentals.
• Observability: OpenTelemetry concepts, including spans, context propagation and resource attributes, instrumentation with LLM observability platforms such as Langfuse or LangSmith, and structured logging that supports investigation.
• Cloud and delivery: Working knowledge of Azure, including private endpoints, DNS and Log Analytics. Docker and Kubernetes application deployment, including Helm and workload identity, and CI/CD pipelines such as Azure DevOps. Working proficiency in Terraform or comparable infrastructure-as-code tooling.
• AI-native delivery: Fluent use of AI coding agents such as Claude Code, Codex, GitHub Copilot or Cursor to accelerate design, coding, testing and documentation, with every output reviewed, tested and understood before it is submitted.
• Communication: Professional command of spoken and written English, with the ability to explain designs, problems and trade-offs clearly to technical and non-technical audiences, give and receive feedback articulately, restate technical direction accurately, and raise risks early and in writing.
• Technical writing: Authoring design documents, technical specifications, runbooks and decision records that are accurate, concise and usable by engineers, reviewers and AI coding tools.
• Enterprise delivery: Working within security review, open-source approval and private network constraints, and delivering through reviewed pull requests with tests and documentation.