{"schemaVersion":"jobsearcher.job.v1","id":"b987ebef85dccf8152a2b4ce","url":"https://jobsearcher.com/jobs/b987ebef85dccf8152a2b4ce","canonicalUrl":"https://jobsearcher.com/jobs/b987ebef85dccf8152a2b4ce","title":"AgentOps / MLOps Engineer","description":"AgentOps / MLOps EngineerLocations are Austin, Charlotte, San Diego- Onsite12 Months ContractExperience: 7+ years in platform / DevOps / MLOps engineering, including production LLM or ML workloads.You turn a working pipeline into a production system. The existing toolchain needs to be fully wired into CI/CD. You will enhance it with a proper evaluation and guardian pattern, and make the whole thing observable, auditable and affordable.ResponsibilitiesProductionise the existing RAG and scanner toolchain through CI/CD — connecting the pipeline end to end so scans, dispositions and remediations flow without manual intervention.Build the guardian / evaluation agent: an automated check that runs on every sub-agent deliverable, replacing the current brute-force knowledge-capture approach with a best-practice evaluation pattern.Implement the deterministic assertion layer as a programmatic gate — automatically rejecting any disposition that contradicts its own evidence, before a human ever sees it.Own AgentOps: trace capture, prompt / rule / model versioning, evaluation-in-CI, regression harnesses, and drift detection.Build the observability the team watches daily: pending burn-down, auto-disposition rate, accuracy against the gold set, human-minutes per item, assertion-rejection rate and cost per item.Own FinOps for the AI workload: model routing, delta-scoped runs (re-processing only items whose evidence changed), caching, and a per-cycle token budget tracked as a service-level objective.Guarantee provenance and auditability for a regulated environment — every decision reproducible from its evidence snapshot, rule/prompt/model version and human verdict.QualificationsPython — production-grade.CI/CD automation for application and ML/LLM workloads; release automation and test gating.AgentOps / LLMOps — tracing, prompt versioning, evaluation in CI, regression harnesses, drift detection.Observability — OpenTelemetry, distributed tracing, metrics and logging; building dashboards operators actually use.AWS; containerisation; infrastructure-as-code (Terraform).FinOps for AI workloads — token accounting, model-routing economics, cost dashboards.Guardrails and policy-as-code; secure handling of regulated data.Working knowledgeKubernetes; LangGraph; AWS Bedrock Guardrails.SQL; Informatica; evaluation-harness construction.If interested, Kindly reply with the following details to Email- jnehru@nam-it.comVisa StatusCurrent LocationResume","company":"Nam Info","rawCompany":"nam info","city":"Austin","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-08-01T10:06:49.791Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"AgentOps / MLOps Engineer","description":"AgentOps / MLOps EngineerLocations are Austin, Charlotte, San Diego- Onsite12 Months ContractExperience: 7+ years in platform / DevOps / MLOps engineering, including production LLM or ML workloads.You turn a working pipeline into a production system. The existing toolchain needs to be fully wired into CI/CD. You will enhance it with a proper evaluation and guardian pattern, and make the whole thing observable, auditable and affordable.ResponsibilitiesProductionise the existing RAG and scanner toolchain through CI/CD — connecting the pipeline end to end so scans, dispositions and remediations flow without manual intervention.Build the guardian / evaluation agent: an automated check that runs on every sub-agent deliverable, replacing the current brute-force knowledge-capture approach with a best-practice evaluation pattern.Implement the deterministic assertion layer as a programmatic gate — automatically rejecting any disposition that contradicts its own evidence, before a human ever sees it.Own AgentOps: trace capture, prompt / rule / model versioning, evaluation-in-CI, regression harnesses, and drift detection.Build the observability the team watches daily: pending burn-down, auto-disposition rate, accuracy against the gold set, human-minutes per item, assertion-rejection rate and cost per item.Own FinOps for the AI workload: model routing, delta-scoped runs (re-processing only items whose evidence changed), caching, and a per-cycle token budget tracked as a service-level objective.Guarantee provenance and auditability for a regulated environment — every decision reproducible from its evidence snapshot, rule/prompt/model version and human verdict.QualificationsPython — production-grade.CI/CD automation for application and ML/LLM workloads; release automation and test gating.AgentOps / LLMOps — tracing, prompt versioning, evaluation in CI, regression harnesses, drift detection.Observability — OpenTelemetry, distributed tracing, metrics and logging; building dashboards operators actually use.AWS; containerisation; infrastructure-as-code (Terraform).FinOps for AI workloads — token accounting, model-routing economics, cost dashboards.Guardrails and policy-as-code; secure handling of regulated data.Working knowledgeKubernetes; LangGraph; AWS Bedrock Guardrails.SQL; Informatica; evaluation-harness construction.If interested, Kindly reply with the following details to Email- jnehru@nam-it.comVisa StatusCurrent LocationResume","datePosted":"2026-08-01T10:06:49.791Z","dateModified":"2026-08-01T10:06:49.791Z","hiringOrganization":{"@type":"Organization","name":"Nam Info","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Austin","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"b987ebef85dccf8152a2b4ce"},"url":"https://jobsearcher.com/jobs/b987ebef85dccf8152a2b4ce"}}