2 months ago
Machine Learning Ops Engineer (AI)
11 667 - 14 583$
Vacancy from Hirify Global, list of international tech companiesPlus is required to make matches and apply
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Job description
Text:
TL;DR
Machine Learning Ops Engineer (AI): Building and optimizing LLM-powered tooling, RAG systems, and agentic workflows to automate internal productivity with an accent on retrieval quality and agent design. Focus on deploying AI services on Kubernetes, implementing secure data boundaries, and establishing rigorous evaluation frameworks for LLM outputs.
Location: Must be based in the United States (required for E-Verify and security clearance eligibility)
Salary: $140,000 - $175,000 USD
Company
develops cutting-edge autonomous solutions and unmanned aircraft systems (UAS) for the defense and aerospace sectors.
What you will do
- Design and build LLM-powered tools and agentic workflows to automate company-wide productivity.
- Improve RAG systems, including ingestion, chunking, embedding, and ranking, to enhance answer quality.
- Deploy and operate AI infrastructure on Kubernetes, managing model serving, inference endpoints, and APIs.
- Implement secure access boundaries and integrate with identity systems to prevent cross-team data leakage.
- Build CI/CD pipelines and automate configuration using Ansible and infrastructure-as-code practices.
- Establish evaluation and testing frameworks to measure LLM quality and guide iteration.
Requirements
- Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or equivalent industry experience.
- 3-6+ years of experience in MLOps, software, platform, or backend engineering.
- Strong proficiency in Python and experience shipping and operating production services.
- Experience building LLM applications using LLM APIs, prompt engineering, and tool/function calling.
- Hands-on experience with Kubernetes and containerized deployment.
- Ability to obtain or maintain a security clearance and verify US work eligibility via E-Verify.
Nice to have
- Experience with vector databases (pgvector, Qdrant, Weaviate, Milvus) and orchestration libraries (LangChain, LlamaIndex).
- Familiarity with the Model Context Protocol (MCP) and model optimization tools (vLLM, TGI, Triton).
- Experience implementing fine-grained authorization (OAuth, SSO, RBAC).
- Knowledge of GPU infrastructure and scheduling for training or inference.
- Background in security and data-handling requirements for regulated or defense environments.
Culture & Benefits
- Comprehensive benefit package including medical, dental, vision, and life insurance.
- 401k with company-match.
- 4 weeks of paid time off per year plus 12 annual company holidays.
- Collaborative environment working on high-impact technology for the future of defense.
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