AgentOps / MLOps Engineer

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