NAM Info Inc
AgentOps / MLOps Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AgentOps / MLOps Engineer, 12-month contract, onsite in Austin, Charlotte, or San Diego. Requires 7+ years in MLOps engineering, expertise in Python, CI/CD, AWS, and observability. Strong FinOps and regulatory experience needed.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
August 1, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Austin, TX
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🧠 - Skills detailed
#Informatica #ML (Machine Learning) #Terraform #Python #Automation #AWS (Amazon Web Services) #Regression #AI (Artificial Intelligence) #Kubernetes #DevOps #SQL (Structured Query Language) #Observability #Logging
Role description
AgentOps / MLOps Engineer
Locations are Austin, Charlotte, San Diego- Onsite
12 Months Contract
Experience: 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.
Responsibilities
Productionise 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.
Qualifications
Python — 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 knowledge
Kubernetes; LangGraph; AWS Bedrock Guardrails.
SQL; Informatica; evaluation-harness construction.
If interested, Kindly reply with the following details to Email- jnehru@nam-it.com
Visa Status
Current Location
Resume
AgentOps / MLOps Engineer
Locations are Austin, Charlotte, San Diego- Onsite
12 Months Contract
Experience: 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.
Responsibilities
Productionise 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.
Qualifications
Python — 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 knowledge
Kubernetes; LangGraph; AWS Bedrock Guardrails.
SQL; Informatica; evaluation-harness construction.
If interested, Kindly reply with the following details to Email- jnehru@nam-it.com
Visa Status
Current Location
Resume
