Lead Machine Learning Engineer

Description :

Job description :

Hiring for Product Based & US Based MNC

Location : Mumbai {for outer location remote is available}

Roles & Responsibilities :

Establish and Implement MLOps practices :

- Development of end-to-end MLOps framework and Machine Learning Pipeline using GCP, Vertex AI, and Software tools

- Serving Pipeline with multiple creation Vertex AI and GCP services.

- Improve ML pipeline documentation and understandability.

- Automate logging of model usage and predictions provided. Improve logging and diagnostic processes

- Automate monitoring of models both for failures and degradation.

- Automate monitoring of data sources to identify issues and/or data changes.

- Design and implement dynamic re-training of ML pipelines using event-based or custom logic

- Resource and Infra Monitoring configuration and pipeline development using GCP service.

- Branching strategies and Version Control using GitHub

- ML Pipeline orchestration and configuration using Airflow/Kubeflow.

- Code refactorization & coding best practices implementation as per industry standard

- Implementing MLOps practices on a project and establishing MLOps best practices.

- Lead the investigation and resolution of production issues, perform root cause analysis, and recommend changes to reduce/eliminate re-occurrence of issues.

- Optimize deployment and change control processes for models.

- Create and operationalize quality assurance processes for ML models

Lead the execution of ML Solutions @Scale :

- Partners with business stakeholders to design the right deliver value-added insights and intelligent solutions through ML and AI.

- Collaborates with Data Science Leads, ML System Engineering and Platform teams to ensure the models are deployed in a scaled and optimized way. Additionally, ensure support the post-production to ensure model performance degrades are proactively managed.

- Play a lead role in spearheading the development effort of new standards (design patterns, coding practices, orchestration patterns) and drive value and adoption across the Data Science team

- Is considered an expert in the ML Ops and Model management space; brings together business knowledge, architecture, resources, people, and technology to create more effective solutions

Research, Evolve and Publish best practices :

- Research and operationalize technology and processes necessary to scale ML Ops

- Recommend model changes to optimize cloud spend.

- Ability to research and recommend MLOps best practices on new technologies, platforms, and services.

- Drive ideation, design, and creation of new ML Architecture patterns in discussion with the Enterprise Architecture team.

- MLOps pipeline improvement plan and suggestion

Communication and Collaboration :

- Knowledge sharing with the broader analytics team and stakeholders.

- Communicate on the on-goings to embrace the remote and geographical culture.

- Ability to communicate the accomplishments, failures, and risks in timely manner.

- Knowledge sharing session with team for specific ML Ops topics. Coach and Mentor junior ML members in the team.

- Foster a collaborative and innovative team environment. Contribute to the overall effort to educate stakeholders on AI practices.

- Closely collaborates with the stakeholders on projects and data science leaders to ensure practices are developed and enhanced to support accelerated analytic development and maintainability.

Embrace a learning mindset :

- Continually invest in ones knowledge and skillset through formal training, reading, and attending conferences and meetups

Good to have skills :

- GCP Machine Learning certification

- Understanding of CPG industry

- Exposure to Deep Learning/RL/LLMs

- Prior experience with CPG industry.

- Publications or contributions to the data science and AI community.

- Certifications in AI, machine learning, or related fields.

Technical Skill proficiency expectations :

Expert Level :

- ML Ops framework

- Big Query/SQL

- Python / R

- Vertex AI and GCP Services

- Docker-Container

- ML Orchestrator

- Kubeflow/Airflow

- GitHub

- Strong communication skills

Intermediate Level :

- Machine Learning and Deep Learning algorithms

- Agile techniques

- Demonstrates teamwork skills.

- Mentor others and lead best practices.

- Understanding of ML Architecture

Basic Level :

- Consumer Packaged Goods domain knowledge

- Large Language Models and deployment architecture

- Graph database

- Tools like Neptune.AI/ML Flow

- Feature Store (GCP Vertex Feature Store, Feast etc)