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Ml Ops Engineer

Posted on: Nov 10, 2026

Apptad Inc
Bellevue, Washington
Onsite
CTC

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Job Title :

Ml Ops Engineer

Job Type :

Contract

Job Location :

Bellevue Washington United States

Remote :

Job Description :

JOB DESCRIPTION

MLOps / LLMOps Engineer

Takes our Python-based LLM, RAG and agent systems from code into reliable, secure, observable production on Azure. The focus is deploying, automating and monitoring these systems, not building features.

EXPERIENCE	RELEVANT	CLOUD	CORE LANGUAGE
5-8 years total	3+ yrs MLOps / DevOps for ML	Microsoft Azure	Python 3.11+

What this person will do

Deploy and run containerized FastAPI inference and agent services on Azure.

Build and schedule data, embedding and re-indexing pipelines with Prefect.

Set up LLM observability and evaluation with Langfuse, and gate releases on evaluation results.

Manage Azure OpenAI capacity: rate limits, token budgets, cost and fallbacks.

Operate the data layer: vector databases (Qdrant / Milvus), PostgreSQL, Snowflake, Redis.

Own CI/CD, infrastructure as code, secrets and security for the platform.

Must-have skills (screen out if missing)

Pipeline orchestration	Prefect 2.x/3.x: deployments, work pools, scheduling, blocks. Incremental, idempotent ETL.
Containers & CI/CD	Docker, AKS or Azure Container Apps. GitHub Actions or Azure DevOps pipelines.
Model & LLM serving	FastAPI services in production: async, streaming (SSE), concurrency control, NGINX, Redis.
LLMOps & observability	Langfuse tracing, evals and prompt versioning. Azure OpenAI rate limits, token budgets, retries.
Azure security	Service principals, managed identity, Key Vault, private endpoints, OAuth 2.0 / MSAL.
Data & vector stores	Qdrant or Milvus operations, PostgreSQL, Alembic migrations, Snowflake, Azure Blob.

Good-to-have (strong plus)

Infrastructure as code: Terraform or Bicep.

Model lifecycle: MLflow or Azure ML for model registry, experiment tracking, and running fine-tuning and training jobs.

Monitoring: Azure Monitor / App Insights or Prometheus / Grafana, alerting, drift detection.

RAG evaluation: faithfulness, relevance, NDCG and MRR run as automated pipelines.

Working knowledge of LangGraph / LangChain and MCP servers, enough to deploy and debug them.

Code quality: pytest, Ruff, pre-commit, SAST in CI.

Nice-to-have (bonus)

Ray; PyArrow / Parquet tuning; Splunk; ServiceNow; Microsoft Graph API; Network operations / NOC domain.

Not required for this role

React / TypeScript frontend, deep agent or prompt design, graph algorithms (NetworkX), regex engineering, and building enterprise integrations. These sit with the AI Specialist role.

Red flags when screening profiles

Only notebooks, data science or model research, with no production deployment.

Only AWS or GCP with no Azure exposure (acceptable only if very strong otherwise).

Pure DevOps with no ML, LLM or data pipeline work.

Position Details

Posted:

Nov 10, 2026

Reference Number:

34327-5039

Employment:

CTC

City:

Bellevue

Job Origin:

CIEPAL_ORGANIC_FEED

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JOB DESCRIPTION

MLOps / LLMOps Engineer

EXPERIENCE	RELEVANT	CLOUD	CORE LANGUAGE
5-8 years total	3+ yrs MLOps / DevOps for ML	Microsoft Azure	Python 3.11+

What this person will do

Deploy and run containerized FastAPI inference and agent services on Azure.

Build and schedule data, embedding and re-indexing pipelines with Prefect.

Set up LLM observability and evaluation with Langfuse, and gate releases on evaluation results.

Manage Azure OpenAI capacity: rate limits, token budgets, cost and fallbacks.

Operate the data layer: vector databases (Qdrant / Milvus), PostgreSQL, Snowflake, Redis.

Own CI/CD, infrastructure as code, secrets and security for the platform.

Must-have skills (screen out if missing)

Pipeline orchestration	Prefect 2.x/3.x: deployments, work pools, scheduling, blocks. Incremental, idempotent ETL.
Containers & CI/CD	Docker, AKS or Azure Container Apps. GitHub Actions or Azure DevOps pipelines.
Model & LLM serving	FastAPI services in production: async, streaming (SSE), concurrency control, NGINX, Redis.
LLMOps & observability	Langfuse tracing, evals and prompt versioning. Azure OpenAI rate limits, token budgets, retries.
Azure security	Service principals, managed identity, Key Vault, private endpoints, OAuth 2.0 / MSAL.
Data & vector stores	Qdrant or Milvus operations, PostgreSQL, Alembic migrations, Snowflake, Azure Blob.

Good-to-have (strong plus)

Infrastructure as code: Terraform or Bicep.

Model lifecycle: MLflow or Azure ML for model registry, experiment tracking, and running fine-tuning and training jobs.

Monitoring: Azure Monitor / App Insights or Prometheus / Grafana, alerting, drift detection.

RAG evaluation: faithfulness, relevance, NDCG and MRR run as automated pipelines.

Working knowledge of LangGraph / LangChain and MCP servers, enough to deploy and debug them.

Code quality: pytest, Ruff, pre-commit, SAST in CI.

Nice-to-have (bonus)

Ray; PyArrow / Parquet tuning; Splunk; ServiceNow; Microsoft Graph API; Network operations / NOC domain.

Not required for this role

React / TypeScript frontend, deep agent or prompt design, graph algorithms (NetworkX), regex engineering, and building enterprise integrations. These sit with the AI Specialist role.

Red flags when screening profiles

Only notebooks, data science or model research, with no production deployment.

Only AWS or GCP with no Azure exposure (acceptable only if very strong otherwise).

Pure DevOps with no ML, LLM or data pipeline work.

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Ml Ops Engineer