Machine Learning; MLOps Engineer

Client Overview

Our client is a fast-growing technology-driven organization focused on building scalable AI and machine learning solutions that power business-critical applications. They are looking to add a Machine Learning (MLOps) Engineer to their team. Their teams work across cloud infrastructure, data engineering, software development, and AI/ML platforms to deliver innovative products in a collaborative and fast-paced environment. They invest heavily in modern cloud technologies, automation, and data-driven decision-making.

$140k - $190k (Dependent on experience and technical expertise)

Our client is seeking a highly skilled Machine Learning (MLOps) Engineer to support the deployment, automation, monitoring, and scalability of enterprise machine learning systems. This role will partner closely with Data Scientists, Software Engineers, Dev Ops teams, and business stakeholders to operationalize ML models in production environments. The ideal candidate has strong experience building CI/CD pipelines for ML workflows, managing cloud-native infrastructure, and supporting end-to-end machine learning lifecycle management.

• Design, build, and maintain scalable MLOps platforms and infrastructure for machine learning model deployment and monitoring.
• Develop and automate CI/CD pipelines for ML training, testing, validation, and production deployment.
• Collaborate with Data Scientists and Engineering teams to product ionize machine learning models and workflows.
• Implement model versioning, experiment tracking, feature stores, and automated retraining pipelines.
• Monitor model performance, drift detection, system reliability, and operational health across production environments.
• Manage cloud infrastructure and containerized applications using Kubernetes, Docker, and Infrastructure-as-Code tools.
• Optimize ML workflows for scalability, performance, security, and cost efficiency.
• Support governance, compliance, and reproducibility standards for enterprise AI systems.
• Troubleshoot infrastructure, deployment, and model performance issues across distributed systems.
• Contribute to platform engineering best practices, automation strategies, and operational documentation.

• 4+ years of experience in Machine Learning Engineering, MLOps, Dev Ops, or Platform Engineering roles.
• Strong experience deploying and managing ML models in production environments.
• Hands‑on expertise with Python and ML frameworks such as Tensor Flow, PyTorch, or Scikit-learn.
• Experience building CI/CD pipelines using tools such as Git Hub Actions, Jenkins, Git Lab CI, or ArgoCD.
• Proficiency with Docker, Kubernetes, and container orchestration platforms.
• Strong cloud experience with AWS, Azure, or Google Cloud Platform.
• Experience with ML lifecycle and orchestration tools such as MLflow, Kubeflow, Sage Maker, Vertex AI, or Airflow.
• Familiarity with Infrastructure-as-Code tools, including Terraform or Cloud Formation.
• Strong understanding of distributed systems, APIs, microservices, and production monitoring.
• Experience with logging and observability tools such as Prometheus, Grafana, Datadog, or ELK Stack.
• Strong communication and cross‑functional collaboration skills.

• Experience supporting Generative AI, LLMOps, or Agentic AI platforms.
• Familiarity with vector databases, RAG pipelines, and AI orchestration frameworks.
• Experience working in highly regulated environments such as finance, healthcare, or enterprise SaaS.
• Knowledge of data engineering technologies such as Spark, Kafka, or Snowflake.
• Exposure to GPU infrastructure and model optimization techniques.
• Experience implementing security and governance controls for AI/ML systems.
• Kubernetes certifications or cloud platform certifications are preferred.

• Bachelor’s degree in Computer Science, Engineering, Data Science, Information Technology, or a related technical field is required.
• Master’s degree is preferred.

• Competitive salary and annual performance bonus.
• Comprehensive medical, dental, and vision coverage.
• 401(K) with company match.
• Flexible PTO and paid holidays.
• Hybrid work flexibility.
• Professional development and certification reimbursement.
• Employee wellness programs.
• Access to cutting‑edge AI/ML technologies and cloud platforms.
• Collaborative and growth‑oriented engineering culture.