Talent Groups
MLOPS Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an MLOps Engineer in Bolingbrook, IL, on a contract basis. Key skills include Python, CI/CD, Docker, Kubernetes, and experience with LLM, RAG, and predictive ML systems. Security mindset and knowledge of Vertex AI and Big Query are essential.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
January 29, 2026
🕒 - Duration
Unknown
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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
Bolingbrook, IL
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🧠 - Skills detailed
#Scala #Cloud #Monitoring #Dataflow #Data Engineering #Deployment #ML (Machine Learning) #AI (Artificial Intelligence) #"ETL (Extract #Transform #Load)" #Python #Security #Docker #Batch #Kubernetes
Role description
Role: MLOps Engineer
Location: Bolingbrook, IL [ONSITE]
Duration: Contract
Required Skills & Qualifications:
• Strong Python, CI/CD, Docker, Kubernetes.
• Experience operationalizing LLM, RAG, and predictive ML systems.
• Strong foundations in data engineering, schema governance, batch/stream pipelines.
• Security mindset (PII controls, secrets, network boundaries, auditability).
• Vertex AI (ML orchestration & CI/CD, training, tuning, deployment, model registry & monitoring).
• Big Query / Big Query ML (analytics & in‑warehouse ML).
• Cloud Composer + Dataflow (batch/stream ETL orchestration).
• GKE or Cloud Run (secure, scalable model serving).
• Artifact Registry + Cloud Build/Cloud Deploy (container & CI/CD).
Role: MLOps Engineer
Location: Bolingbrook, IL [ONSITE]
Duration: Contract
Required Skills & Qualifications:
• Strong Python, CI/CD, Docker, Kubernetes.
• Experience operationalizing LLM, RAG, and predictive ML systems.
• Strong foundations in data engineering, schema governance, batch/stream pipelines.
• Security mindset (PII controls, secrets, network boundaries, auditability).
• Vertex AI (ML orchestration & CI/CD, training, tuning, deployment, model registry & monitoring).
• Big Query / Big Query ML (analytics & in‑warehouse ML).
• Cloud Composer + Dataflow (batch/stream ETL orchestration).
• GKE or Cloud Run (secure, scalable model serving).
• Artifact Registry + Cloud Build/Cloud Deploy (container & CI/CD).
