Software Developer 2 -- Backend Microservices & Applied AI

Job Description

Description

Hybrid 4 onsite / 1 work from home in Englewood, CO\n
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\nOur client is seeking a Software Developer 2 focused on backend microservices and applied AI. The role splits time between core backend engineering and running proofs of concept on internally built AI tools, as well as building AI-powered tooling that accelerates engineering delivery.\n
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Due to client requirements, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance.

Rate: $40.00 to $50.00/hr. w2\n
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Responsibilities

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• Design, build, and enhance backend microservices and REST APIs that are reliable, observable, and production-grade.

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• Run structured POCs on internally built AI tools and evaluate them against real workflows, providing feedback into the roadmap.

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• Build AI-powered tools and services, including RAG applications, LLM-backed APIs, and lightweight agentic workflows.

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• Integrate LLM capabilities into existing services, including retrieval, prompt design, and grounding or guardrail checks.

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• Partner with senior engineers and product teams to translate promising AI techniques into shippable features.

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• Write clean, tested code and contribute to CI/CD, deployment, and monitoring.
  
  

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Experience Requirements

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• 3+ years building production backend services in Java and Spring Boot or comparable stacks, with strong understanding of microservices and REST API design.

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• Hands-on exposure to applied GenAI such as LLM APIs, RAG pipelines, or agentic frameworks including LangChain, LlamaIndex, or major LLM providers.

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• Working knowledge of Python.

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• Experience with containerization and cloud deployment using Docker and Kubernetes or ECS on AWS or Azure.

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• Comfort with CI/CD and automated testing using tools such as JUnit or pytest and platforms like GitLab, Harness, or Jenkins.

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• Familiarity with vector stores or semantic search such as ChromaDB, pgvector, FAISS, or OpenSearch.

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• Curious and self-driven, comfortable prototyping quickly and learning in public.

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• Nice to have: event-driven architecture with Kafka, LLM guardrails, MLOps exposure, caching and observability tooling, and advanced degree or published ML or AI work.
  
  

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Education Requirements