Description
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: $40.00 to $50.00/hr. w2
Responsibilities
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.
Experience Requirements
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.
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 GitLab, Harness, or Jenkins.
Familiarity with vector stores or semantic search such as ChromaDB, pgvector, FAISS, or OpenSearch.
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.
