Software Developer — Backend Microservices & Applied AI

Hybrid 4 onsite / 1 work from home in Englewood, CO

Our 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.

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: $36.00 to $46.00/hr. w2

• Design, build, and enhance backend microservices and REST APIs that are reliable, observable, and production-grade.
• Run structured POCs on internally built AI tools and evaluate them against real workflows, providing feedback into the roadmap.
• Build AI-powered tools and services, including RAG applications, LLM-backed APIs, and lightweight agentic workflows.
• Integrate LLM capabilities into existing services, including retrieval, prompt design, and grounding or guardrail checks.
• Partner with senior engineers and product teams to translate promising AI techniques into shippable features.
• Write clean, tested code and contribute to CI/CD, deployment, and monitoring.

• 3+ years building production backend services in Java and Spring Boot or comparable stacks, with strong understanding of microservices and REST API design.
• Hands-on exposure to applied GenAI such as LLM APIs, RAG pipelines, or agentic frameworks including Lang Chain, Llama Index, or major LLM providers.
• Working knowledge of Python.
• Experience with containerization and cloud deployment using Docker and Kubernetes or ECS on AWS or Azure.
• Comfort with CI/CD and automated testing using tools such as JUnit or pytest and platforms like Git Lab, Harness, or Jenkins.
• Familiarity with vector stores or semantic search such as ChromaDB, pgvector, FAISS, or Open Search.
• Curious and self-driven, comfortable prototyping quickly and learning in public.
• 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.