Sr. DevOps Engineer (MLOps & DataOps)

Job title: Sr. DevOps Engineer (MLOps & DataOps) in Lakewood, CO at Artech Information Systems
Company: Artech Information Systems
Job description: Role: Sr. DevOps Engineer( MLops & DataOps)
Location: LakeWood, CO
Rate: 70/hr on W2.We are seeking a Senior DevOps Engineer with specialized experience in MLOps and DataOps to support our data science, data engineering, and analytics teams. This hybrid role will focus on designing and automating end-to-end deployment pipelines for both machine learning models and data products. The ideal candidate will support the full lifecycle of ML and data systems, including data ingestion, processing, validation, deployment, monitoring, and governance in a secure and scalable cloud-native environment.
You will collaborate cross-functionally with Data Engineers, ML Engineers, Analysts, and IT teams to build a modern platform for deploying data pipelines, machine learning models, and high-quality data products across the organization.MLOps Focus

• Build and manage CI/CD pipelines for ML model training, validation, packaging, and deployment using tools like Azure DevOps, MLflow, and Azure ML, including model versioning, validation, and deployment across development, staging, and production environments.
• Design, implement, and maintain CI/CD pipelines tailored to ML workflows, Automate the provisioning of cloud infrastructure (Azure, Terraform, ARM) to support ML model training, batch inference, and real-time serving.
• Automate provisioning and configuration of ML training and inference infrastructure using Terraform, ARM templates, and container orchestration (Docker, Kubernetes, Helm).
• Monitor deployed models for drift, performance, and regulatory compliance; integrate observability into ML lifecycle (e.g., Prometheus, Grafana, Azure Monitor).
• Enable version control and governance for data sets, experiments, models, and ML artifacts using ML registries and tracking frameworks.
• Lead initiatives to standardize MLOps practices, improve collaboration between data science, engineering, and IT teams, and optimize the release process of AI/ML-based features.

DataOps & Data Engineering Focus

• Design and maintain robust CI/CD pipelines for data pipelines (ETL/ELT) developed in platforms like Azure Data Factory, Databricks, or Apache Spark.
• Automate data pipeline testing and validation (schema checks, data quality checks, lineage tracking) using tools like Great Expectations or Soda.
• Integrate data quality enforcement and alerting into DevOps workflows to proactively identify and resolve issues in batch and streaming pipelines.
• Ensure secure, compliant, and reproducible data pipeline deployments across dev/test/prod environments.
• Support automated orchestration and release of reusable data assets, data marts, and analytics datasets (Data Products) aligned with a Data Mesh or Product-centric architecture.

Infrastructure & Operations

• Manage cloud-native infrastructure for ML and data workloads in Azure, supporting scale, performance, and security needs.
• Integrate automated testing (unit, integration, performance, and security) into ML pipelines.
• Implement Infrastructure as Code (IaC) practices for reproducibility, consistency, and auditability of environments.
• Collaborate with Cybersecurity and IT to ensure secure access to data and models, implement policy enforcement, secrets management, and audit logging.
• Lead incident response and root cause analysis for failures across ML and data pipelines.
• Provide technical mentorship and leadership in best practices for DevOps and MLOps to teams across the organization.

OTHER DUTIES AND RESPONSIBILITIES

• Champion DevOps best practices across data and ML teams (version control, automation, monitoring, rollback strategies, testing).
• Partner with software, quality, and infrastructure teams to align CI/CD practices across applications, data, and models.
• Document deployment architectures, playbooks, and standard operating procedures.
• Provide technical mentorship and contribute to the development of internal frameworks and templates for DataOps and MLOps.
• Collaborate closely with team members to achieve collective goals
• Contribute to a positive and productive team environment.
• Provide organizational mentorship on DevOps/MLOps standards, best practices and patterns
• Stay current with emerging MLOps tools and practices, and evaluate their applicability to business needs.
• Create and maintain technical documentation for MLOps frameworks, tools, and workflows.
• Partner with software, cybersecurity, infrastructure, and quality teams to ensure seamless integration and deployment of ML services.
• Promote adherence to Agile and DevOps principles throughout machine learning product lifecycles.

MINIMUM QUALIFICATION REQUIREMENTS
Education:

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Experience:

• 6+ years in a DevOps role with experience supporting Data Engineering and/or MLOps environments.

• Strong hands-on experience with CI/CD for both code and data pipelines (Azure DevOps, Terraform, ArgoCD) in MLOpd and Data Ops contexts
• Familiarity with data platform tools like Azure Data Factory, Databricks, Spark, Delta Lake
• Proven experience deploying and supporting ML and data products in regulated or compliance-driven environments.

Skills:

• Proficient in scripting (Python, PowerShell, YAML) and IaC (Terraform, ARM templates).
• Experience with DevOps toolchains: Git, Azure DevOps, GitHub Actions, ArgoCD, Helm, Docker, Kubernetes.
• Familiarity with data quality tools (Great Expectations, Soda), ML tools (MLflow, Azure ML), and monitoring systems (Grafana, Prometheus).
• Excellent problem-solving, collaboration, and documentation skills.
• Strong communication skills and a team-first mindset.
• Deep knowledge of cloud-native environments and deployment of ML services using containers and microservices.
• Strong understanding of ML model lifecycle, including data versioning, model tracking, and experiment management.
• Familiarity with regulatory concerns and best practices for deploying ML in healthcare or similarly regulated industries.
• Excellent communication skills and the ability to collaborate effectively across technical and non-technical teams.

PREFERRED QUALIFICATIONS

• Experience deploying models in edge or hybrid cloud environments.
• Knowledge of feature stores, model registries, and online/offline inference optimization.
• Experience with cybersecurity in ML pipelines (e.g., securing APIs, audit logging, model integrity checks).

Certificates, Licenses, RegistrationsPHYSICAL REQUIREMENTS
Typical Office Environment requirements include reading, speaking, hearing, close vision, traverse, bending, sitting, and occasional lifting up to 20 pounds.The physical demands described here are representative of those that must be met by an associate to successfully perform the essential duties of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential duties.Additional Physical Requirements
Hybrid onsite/remote work allowed: Min of 3 days a week in our Lakewood offices, 2 from home.Regards,
Sourav Paul
Technical RecruiterCell: 3125840961
Email:
360 Mt. Kemble Avenue, Suite 2000, Morristown, NJ 07960
Expected salary: $70 per hour
Location: Lakewood, CO
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