ML Ops Engineer

3 days ago

Hyderabad, Telangana, India Techno Facts Solutions Full time ₹ 12,00,000 - ₹ 36,00,000 per year

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

Certifications: 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.

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