ML Ops Engineer

Job Responsibilities

• Evaluate and source appropriate cloud infrastructure solutions for machine learning

needs, ensuring cost-effectiveness and scalability based on project requirements.

• Automate and manage the deployment of machine learning models into production

environments, ensuring version control for models and datasets using tools like

Docker and Kubernetes.

• Set up monitoring tools to track model performance and data drift, conduct regular

maintenance, and implement updates for production models.

• Work closely with data scientists, software engineers, and stakeholders to align on

project goals, facilitate knowledge sharing, and communicate findings and updates to

cross-functional teams.

• Design, implement, and maintain scalable ML infrastructure, optimizing cloud and on-

premise resources for training and inference.

• Document ML processes, pipelines, and best practices while preparing reports on

model performance, resource utilization, and system issues.

• Provide training and support for team members on ML Ops tools and methodologies,

and stay updated on industry trends and emerging technologies.

• Diagnose and resolve issues related to model performance, infrastructure, and data

quality, implementing solutions to enhance model robustness and reliability.

Education, Technical Skills & Other Critical Requirement

• 3-6 years of relevant experience in AI/ analytics product & solution delivery
• Bachelors/Masters degree in an information technology/computer science/

Engineering or equivalent fields experience.

• Proficiency in frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Strong skills in Python and/or R; familiarity with Java, Scala, or Go is a plus.
• Experience with cloud services such as AWS, Azure, or Google Cloud Platform,

particularly in ML services (e.g., AWS SageMaker, Azure ML). Databricks is a must

have.

• CI/CD tools (e.g., Jenkins, GitLab CI), containerization (e.g., Docker), and

orchestration (e.g., Kubernetes).

• Experience with databases (SQL and NoSQL), data pipelines, ETL processes, ML

pipeline orchestration (Airflow)

• Familiarity with monitoring and logging tools such as Prometheus, Grafana, or ELK

stack.

• Proficient in using Git for version control.
• Strong analytical and troubleshooting abilities to diagnose and resolve issues

effectively.

• Good communication skills for working with cross-functional teams and conveying

technical concepts to non-technical stakeholders.

• Ability to manage multiple projects and prioritize tasks in a fast-paced environment.

Preferred:

• Familiarity of new age AI and ML techniques such as GenAI, foundational models,

large language models (LLMs) and applications

• Relevant certifications in cloud platforms, machine learning, or data science (e.g.,

AWS Certified Machine Learning, Azure Data Engineer).

• Prior experience in MlOps
• Prior experience in P&C Insurance

Databricks Certified Machine Learning, Databricks Certified Data Engineer Professional, Microsoft Certified: Azure AI Fundamentals (AI-900), Microsoft Certified: Azure AI Engineer Associate (AI-102), Microsoft Certified: Azure Data Scientist Associate (DP-100). Prioritize candidates with any of these certifications.