About The Position

Navitas is seeking a Python Developer / Full Stack Engineer to build and scale user-facing and backend applications that power AI/ML capabilities for the DoD Search Portfolio. You will develop secure, cloud-native web services and UI experiences that integrate with LLM/RAG and semantic search pipelines. This role partners closely with ML engineers and data teams to productionize models through robust APIs, workflows, and operational tooling. You will help deliver mission-ready solutions that handle large-scale datasets with strong performance, reliability, and traceability. The ideal candidate thrives in fast-paced environments and enjoys building end-to-end systems that make AI usable for real users.

Requirements

  • Bachelor’s degree (or equivalent experience) with 5+ years in software engineering focused on Python and web application development.
  • Strong hands-on experience building Python APIs/services (Flask/FastAPI preferred) and integrating with databases, search platforms, and external systems.
  • Experience developing full stack applications using modern front-end frameworks (React/Angular/Vue) plus strong API design skills.
  • Familiarity with AI/ML concepts and integrations (LLMs, embeddings, semantic search, RAG workflows) and working closely with data/ML teams.
  • Working experience with cloud-native delivery (Docker/Kubernetes), CI/CD, Git-based workflows, and performance/reliability best practices; Databricks/Spark is a plus.

Responsibilities

  • Design and develop Python backend services and REST APIs (e.g., Flask/FastAPI) to expose AI/ML and search capabilities to applications and mission systems.
  • Build full stack features (UI + API) that support search workflows, semantic retrieval, and results visualization for enterprise users.
  • Integrate backend services with Databricks/Spark pipelines and ML workflows to enable scalable inference, data processing, and batch/stream execution.
  • Implement and maintain integrations with Elasticsearch (search/indexing) and Neo4j (graph/relationship-driven experiences) to enhance relevance and discovery.
  • Apply best practices for security, logging, auditing, and compliance aligned with federal/DoD standards across services and environments.
  • Collaborate with ML Engineers to productionize LLM/RAG-based features, including prompt/inference orchestration, embeddings services, and retrieval workflows.
  • Support CI/CD and DevOps practices (containerization, deployment automation, monitoring/alerting) to ensure stable releases and operational readiness.
  • Create clear technical documentation and communicate design decisions, trade-offs, and implementation approaches to both technical and non-technical stakeholders.
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