Python Developer

Programmers.io; Phoenix (AZ); On-site; USD 140,000 - 190,000; Full time

Job summary

Programmers.io is seeking an experienced AI Platform Engineer to design, build, and maintain enterprise knowledge-base infrastructure and AI-enabled backend services using Python and FastAPI. You will develop scalable ingestion, embedding, and vector-based retrieval pipelines, and implement LangChain/LangGraph workflows with robust tool integration and state management.

You will drive secure LLM integrations, optimize performance and reliability, and contribute to Docker/Kubernetes deployments,

Qualifications

• Experience building secure, scalable REST APIs and microservices with FastAPI.
• Strong knowledge of asynchronous Python and API design.
• Experience with LangChain or LangGraph for agent workflows.
• Production experience with Docker and Kubernetes.

Responsibilities

• Design, develop, and maintain enterprise knowledge-base infrastructure and AI-enabled backend services.
• Build and optimize ingestion, embedding, and vector indexing pipelines for grounded retrieval.
• Develop agent workflows and tool integrations with secure API patterns.

Skills

Python API Engineering

FastAPI REST APIs

Async Python/Design

LangChain/LangGraph

PostgreSQL pgvector

Docker & Kubernetes

GitHub Actions CI/CD

OpenTelemetry & Observability

Secure Enterprise Data

Tools

Docker

Kubernetes

Job description

Role Descriptions

• Knowledge Base & AI Platform Engineering
• Design, develop, maintain and evolve enterprise knowledge-base infrastructure and AI-enabled backend services using Python and FastAPI.
• Build and optimize ingestion, chunking, embedding, vector indexing, semantic retrieval, reranking, context construction and grounded RAG pipelines.
• Develop LangChain and/or LangGraph-based agent workflows and reusable agent-runtime capabilities including tool calling, state management, retries, validation, guardrails and human approval where required.
• Integrate LLMs and enterprise knowledge sources securely through reusable APIs and services; use MCP or similar agent tool integration patterns where appropriate.
• Implement PostgreSQL/vector database persistence, metadata filtering and retrieval patterns optimized for accuracy, latency and scale.

Cloud-Native Delivery & Production Engineering

• Containerize services with Docker and deploy/manage them on Kubernetes.
• Build and maintain CI/CD pipelines using GitHub Actions, Jenkins or equivalent tools, including automated testing and deployment controls.
• Implement logging, tracing, metrics, dashboards and alerts using OpenTelemetry, Prometheus, Grafana and/or enterprise monitoring tools.
• Diagnose and resolve issues across APIs, retrieval pipelines, agent execution, databases, containers and distributed infrastructure.
• Improve performance, reliability, security, scalability and cost efficiency of production AI services.

Quality, Evaluation & Collaboration

• Establish automated tests and evaluation approaches for APIs, retrieval quality, grounded responses and agent behavior.
• Apply secure coding, access controls, audit logging, prompt/output validation and responsible-AI practices.
• Partner with product, architecture, data, security, DevOps and application teams to translate business needs into reusable knowledge and AI platform capabilities.
• Document architecture decisions, API contracts, operational procedures and knowledge-platform standards.
• Participate in Agile delivery, code reviews, technical design discussions, mentoring and production support""

Skills Required

• Python & API Engineering
• Strong hands-on experience with Python 3.11+ and production backend development.
• Strong experience building secure, scalable REST APIs and microservices using FastAPI; familiarity with Flask/Django is useful.
• Strong understanding of asynchronous Python, API design, validation, error handling, authentication/authorization, and enterprise integration patterns.

Generative AI, RAG & Knowledge Infrastructure

• Hands-on production experience building enterprise Generative AI applications using LLMs, RAG, semantic search, embeddings, and vector databases; ideally 2+ years of recent GenAI/RAG experience.
• Strong hands-on experience with LangChain and/or LangGraph for agentic workflows, orchestration, tool calling, multi-step execution, and controlled agent runtimes. LangGraph experience is preferred for stateful agent orchestration.
• Experience designing knowledge ingestion, document chunking, embedding generation, metadata enrichment, retrieval/reranking, context assembly, grounded generation, and response validation pipelines.
• Experience with PostgreSQL and vector search. pgvector experience is highly desirable but not mandatory.
• Strong understanding of prompt engineering, AI agents, guardrails, human-in-the-loop patterns, AI evaluation, responsibleAI, and secure enterprise data access. Platform Engineering, DevOps & Observability
• Strong hands-on experience with Docker and Kubernetes for production AI services.
• Experience with Git and CI/CD pipelines using GitHub Actions, Jenkins, or equivalent tools for automated build, test, and deployment.
• Experience with production monitoring and observability using OpenTelemetry, Prometheus, Grafana, Splunk/ELK, or equivalent tools.
• Ability to troubleshoot performance, reliability, retrieval quality, API, infrastructure, and production issues across distributed AI systems.
• Strong analytical, problem-solving, documentation, and cross-functional communication skills. Preferred / Good-to-Have Skills
• LangGraph for stateful agent orchestration and reusable agent-runtime patterns.
• PostgreSQL pgvector or equivalent production vector-database experience.
• Model Context Protocol (MCP), tool integration protocols, or reusable agent-tool interfaces
• OpenTelemetry and production AI/agent observability; GitHub Actions experience.
• Experience with automated RAG/agent evaluation, regression datasets, retrieval-quality metrics, and grounded-response""