Job Title

Budget: 23 lpa Experience: 3-6 years As a Senior MLOps Engineer, you will be responsible for a wide range of engagements, listed below: • Can guide the team to build a scalable MLOps platform and work with business architects to implement and expand the MLOps setup. • Has worked on deploying ML models to production and has experience with model monitoring, inferencing, drift concepts, feature stores, etc. • Has a good understanding of how ML algorithms work and how to debug issues in the code. • Has deployed applications using containers and container orchestration • Manages conversations with client stakeholders to understand requirements and translate them into technical outcomes. • Responsible for bridging the gap between machine learning and software engineering, focusing on deployment, scaling, and management of ML models in production environments. • A developer focused on streamlining the process of developing, deploying, and maintaining ML systems to ensure they are efficient, scalable, and reliable. • Has a strong conceptual understanding of the ML lifecycle and various ML algorithms. • Has worked on at least one cloud platform and its ML-as-a-Service offerings. Required Skills and Experience Technical: • Strong understanding of DevOps practices and skilled in developing end-to-end CI/CD pipelines • Execution knowledge of data modeling, databases (SQL and NoSQL), software development lifecycle practices, unit testing, functional programming, etc. • Experience with one or more ML-as-a-Service platforms, such as Azure DevOps, Dataiku, or equivalent open-source tools • Proficient in at least one programming language used in data engineering, such as Python • Strong knowledge of Snowflake • Experience working on at least one cloud platform