{"schemaVersion":"jobsearcher.job.v1","id":"fa60644be46142d78b7b8a8a","url":"https://jobsearcher.com/jobs/fa60644be46142d78b7b8a8a","canonicalUrl":"https://jobsearcher.com/jobs/fa60644be46142d78b7b8a8a","title":"LLMOps Engineer","description":"Overview\nWe are looking for a skilled LLMOpsEngineer to help build, operate, and optimize the infrastructure and pipelines that power our predictive and generative AI capabilities. The LLMOpsEngineer will work closely with AI Product Engineers, Data Scientists, and senior LLMOps staff to support the deployment, monitoring, and continuous improvement of LLM-based systems. This role is highly technical and hands‑on, with opportunities to grow into senior roles through increasing independence, system ownership, and architectural contributions.\n\nContributions\n\nImplement and maintain core LLM pipelines, including model hosting endpoints, embedding pipelines, retrieval layers, and vector database integrations.\n\nBuild automation for model versioning, dataset management, and configuration tracking to support reproducible and reliable GenAI development.\n\nDevelop monitoring and observability components for LLM-based applications, including metrics dashboards, alerting, and logging of model outputs and performance.\n\nCollaborate with AI Product Engineers to move prototypes into production environments, supporting scalability, testing, and integration with mission systems.\n\nAssist in configuring and optimizing inference runtimes, containers, and microservices to ensure responsive and cost‑efficient model operations.\n\nContribute to CI/CD pipelines that support automated testing, evaluation workflows, and safe deployment of updated prompts, models, and context strategies.\n\nHelp implement and maintain basic ML SecOps guardrails, such as input validation, prompt protections, and output filtering.\n\nParticipate in troubleshooting efforts, root‑cause analysis, and issue resolution for operational incidents involving LLM pipelines or infrastructure.\n\nStay current with emerging tools, libraries, and practices for operationalizing LLMs, including orchestration frameworks and inference accelerators.\n\nDocument system workflows, runbooks, and operational best practices to support team knowledge growth and onboarding.\n\nYou will contribute to the growth of our AI & Data Exploitation Practice!\n\nQualifications\n\nAbility to hold a position of public trust with the U.S. government.\n\nBachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field and 5+ years of experience; OR\n\nMaster’s degree in Computer Science, Data Engineering, Machine Learning, or a related field and 3+ years of experience.\n\n2+ years of experience in software engineering, DevOps, MLOps, cloud engineering, or data engineering, with exposure to LLM or ML model operations.\n\nProficiency in Python and familiarity with LLM-related tools and frameworks such as Hugging Face Transformers, LangChain, LlamaIndex, or similar.\n\nExperience with containerization (Docker) and basic orchestration using Kubernetes or serverless model hosting environments.\n\nHands‑on knowledge of cloud platforms (AWS, Azure, or GCP), including compute, storage, and networking fundamentals for AI workloads.\n\nFamiliarity with CI/CD pipelines, automated testing, and environment provisioning for AI or data systems.\n\nExposure to vector databases, embedding models, and RAG pattern implementations preferred.\n\nUnderstanding of modern DevSecOps principles, security basics, and safe handling of data used in AI pipelines.\n\nStrong debugging and problem‑solving skills with an ability to collaborate in cross‑functional engineering teams.\n\nStrong written and verbal communication skills with the ability to document workflows and explain operational concepts.\n\nExperience working in agile or iterative development environments is a plus.\n\nCompensation\nSteampunk relies on several factors to determine salary, including but not limited to geographic location, contractual requirements, education, knowledge, skills, competencies, and experience. The projected compensation range for this position is $115,000 to $145,000. The estimate displayed represents a typical annual salary range for this position. Annual salary is just one aspect of Steampunk’s total compensation package for employees.\n\n#J-18808-Ljbffr","company":"Steampunkcom","rawCompany":"steampunkcom","city":"Bloomington","state":"IL","isRemote":false,"isActive":true,"createdAt":"2026-07-03T03:21:03.537Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"LLMOps Engineer","description":"Overview\nWe are looking for a skilled LLMOpsEngineer to help build, operate, and optimize the infrastructure and pipelines that power our predictive and generative AI capabilities. The LLMOpsEngineer will work closely with AI Product Engineers, Data Scientists, and senior LLMOps staff to support the deployment, monitoring, and continuous improvement of LLM-based systems. This role is highly technical and hands‑on, with opportunities to grow into senior roles through increasing independence, system ownership, and architectural contributions.\n\nContributions\n\nImplement and maintain core LLM pipelines, including model hosting endpoints, embedding pipelines, retrieval layers, and vector database integrations.\n\nBuild automation for model versioning, dataset management, and configuration tracking to support reproducible and reliable GenAI development.\n\nDevelop monitoring and observability components for LLM-based applications, including metrics dashboards, alerting, and logging of model outputs and performance.\n\nCollaborate with AI Product Engineers to move prototypes into production environments, supporting scalability, testing, and integration with mission systems.\n\nAssist in configuring and optimizing inference runtimes, containers, and microservices to ensure responsive and cost‑efficient model operations.\n\nContribute to CI/CD pipelines that support automated testing, evaluation workflows, and safe deployment of updated prompts, models, and context strategies.\n\nHelp implement and maintain basic ML SecOps guardrails, such as input validation, prompt protections, and output filtering.\n\nParticipate in troubleshooting efforts, root‑cause analysis, and issue resolution for operational incidents involving LLM pipelines or infrastructure.\n\nStay current with emerging tools, libraries, and practices for operationalizing LLMs, including orchestration frameworks and inference accelerators.\n\nDocument system workflows, runbooks, and operational best practices to support team knowledge growth and onboarding.\n\nYou will contribute to the growth of our AI & Data Exploitation Practice!\n\nQualifications\n\nAbility to hold a position of public trust with the U.S. government.\n\nBachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field and 5+ years of experience; OR\n\nMaster’s degree in Computer Science, Data Engineering, Machine Learning, or a related field and 3+ years of experience.\n\n2+ years of experience in software engineering, DevOps, MLOps, cloud engineering, or data engineering, with exposure to LLM or ML model operations.\n\nProficiency in Python and familiarity with LLM-related tools and frameworks such as Hugging Face Transformers, LangChain, LlamaIndex, or similar.\n\nExperience with containerization (Docker) and basic orchestration using Kubernetes or serverless model hosting environments.\n\nHands‑on knowledge of cloud platforms (AWS, Azure, or GCP), including compute, storage, and networking fundamentals for AI workloads.\n\nFamiliarity with CI/CD pipelines, automated testing, and environment provisioning for AI or data systems.\n\nExposure to vector databases, embedding models, and RAG pattern implementations preferred.\n\nUnderstanding of modern DevSecOps principles, security basics, and safe handling of data used in AI pipelines.\n\nStrong debugging and problem‑solving skills with an ability to collaborate in cross‑functional engineering teams.\n\nStrong written and verbal communication skills with the ability to document workflows and explain operational concepts.\n\nExperience working in agile or iterative development environments is a plus.\n\nCompensation\nSteampunk relies on several factors to determine salary, including but not limited to geographic location, contractual requirements, education, knowledge, skills, competencies, and experience. The projected compensation range for this position is $115,000 to $145,000. The estimate displayed represents a typical annual salary range for this position. Annual salary is just one aspect of Steampunk’s total compensation package for employees.\n\n#J-18808-Ljbffr","datePosted":"2026-07-03T03:21:03.537Z","dateModified":"2026-07-03T03:21:03.537Z","hiringOrganization":{"@type":"Organization","name":"Steampunkcom","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Bloomington","addressRegion":"IL","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"fa60644be46142d78b7b8a8a"},"url":"https://jobsearcher.com/jobs/fa60644be46142d78b7b8a8a"}}