.. monitoring of tickets and work towards closure Analyzing the issues and provide estimates Development of functional specification documents for changes and enhancements Issue Resolution based on SLA Co-ordinate with technical team to resolve the issues in the given objects Must be a very good team player .. read more
Lead ML Engineer (MLOPS)
Experience - 08 to 14 Years
Work Location - Trivandrum OR Pune
Key responsibilities -
Enable the successful development and deployment of Machine-Learning applications from conceptualization to production with an emphasis on operations and monitoring.
Detect, analyze, address bottlenecks and painpoints along the ML workflow with patterns and practices that will improve quality and speed.
Listen and engage with the product team members to ensure their adoption in a collaborative and influential fashion so that siloed behavior is prevented.
Follow standard industry processes such as Agile, DevOps, version control, model management, deployment and operation of ML applications.
Keep hands-on by occasionally performing software engineering tasks such as: requirements analysis, design, development, testing, deployment, code maintenance, data pipelines, etc.
Contribute and review architectural and other technical documentation, acting as a sparring partner.
Mentor / train more junior colleagues in areas of expertise.
Minimum Qualifications:
Master's degree or PhD in a quantitative or engineering field like Computer Science, Physics, Mathematics or Statistics.
Fluency in English is a must, German is a plus.
Previous experience in business related functions (e.i. Sales, Operations, Claims, Underwriting, Investment Management, Asset Management, Consulting, Product Development, Finance, Market Management, Digital / Tech etc.) is a plus.
Preferred Qualifications:
At least 5 years of hands-on experience as part of end-to-end ML projects.
Expertise in technical documentation practices (e.g. Arc42).
Knowledge of continuous monitoring of performance of ML applications and tools and environments (Grafana, Prometheus, Kubernetes, CI-CD, etc.).
Advanced understanding of cloud technologies (AWS and Azure).
Good understanding of technical feasibility of data-driven products and services.
Experience coordinating with various technical stakeholders (Engineers, Architects, Data Scientists) to achieve a common goal.
Strong ability to self-organize, take ownership of topics and drive them to delivery together with others team members.
Experience in monitoring data drift in a running ML system.
Insurance knowledge and additional languages are a plus.