ML OPS Engineer

3 days ago

Pune, India Olive Green Consulting Full time

Key Responsibilities:

 Lead end-to-end development and delivery of Machine-Learning applications, emphasizing

operations and monitoring.

 Collaborate with multidisciplinary teams to understand business requirements, identify ML

workflow requirements, improve processes, and implement new features.

 Perform software engineering tasks: requirements analysis, design, implementation, testing,

deployment, code maintenance, etc.

 Develop pipelines for deployment and operation of ML applications, following CI/CD best

practices.

 Implement optimization techniques to ensure efficient model inference, reducing

computational costs and improving response times.

 Collaborate with Back-End Engineers to ensure the infrastructure supports the demands of

the model, especially concerning GPU resources, memory, and storage.

 Contribute to architectural and technical documentation

Requirements:

 Degree in a quantitative or engineering field like Computer Science, Physics, Mathematics,

Statistics, etc.

 3-5 years of relevant professional experience in end-to-end data projects, from data cleaning

to productionizing ML models.

 Several years of Python programming experience and overall strong software development

skills

 Demonstrated knowledge of CI/CD pipelines, Jenkins & GitHub actions.

 Solid understanding of machine learning product lifecycle and the commonly

associated components (MLOps)

 Understanding of multiple machine learning algorithms and techniques.

 Expertise in ML model deployment (familiar with REST APIs) and monitoring.

 Experience with containerization technologies such as Docker and Kubernetes.

 Solid knowledge of at least one major cloud platform, preferably Azure Cloud.

 Experience coordinating with various technical stakeholders (Engineers, Architects, Data

Scientists) to achieve common goals.

 Strong ability to self-organize, take ownership of topics, and drive them to delivery together

with team members.

 Fluency in English is required; additional languages are a plus.

Good to Have:

 Insurance knowledge and proficiency in additional languages.

 Experience in newer ML approaches (LLM, computer vision, etc.).

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