Lead Machine Learning Engineer

1 week ago

Mumbai, Maharashtra, India Career Stone Consultant Private Limited Full time

Role : Lead ML Engineer

Location : Mumbai, Hybrid

In this role, you are a critical member of the data science group focused on leading efforts in migrating ML-based solutions from concept to productionlevel operational excellence.

You will lead initiatives building scalable, resilient, and automated solutions in GCP (Google Cloud Platform) to ensure that models deliver on organizational objectives You will professionally engineer solutions considering notions of risk and FMEA(failure modes and effects analysis).

The ideal candidate will have expertise in AI platforms, ML model development life cycle, model management including orchestration, deployment, and monitoring, GCP Vertex AI, and a proven track record of successful AI solution delivery.

The ML Engineering capability is leveraged to fuel advanced AI/ML solutions driving decisionmaking for critical enterprise needs. It is also responsible for implementing and enhancing the community of practice to determine the best practices, standards, and MLOps frameworks to efficiently deliver enterprise data solutions at General Mills.

This role works in close collaboration with Data Scientists, Data Engineers, Architects and other teams to support the analytic consumption needs. Enhances the performance of the models and automates the production pipelines to gain efficiency.

Job Overview :

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.

Role Responsibilities :

- 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 forspecific 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 leadersto ensure practices are developed and enhanced to support accelerated analytic development and maintainability.

Embrace a learning mindset :

- Continually invest in one's knowledge and skillset through formal training, reading, and attending conferences and meetups

- Advanced degree in a quantitative field (CS, engineering, statistics, math, data science).

- Proven technical leadership in a large, complex matrixed organization.

- Relevant Machine Learning experience of 6+ years and overall 12+ years of Industry experience.

- Experience in supervised ML algorithms, optimization, and performance tuning.

- Track record of producing machine learning models and production infrastructure at scale.

- Strong verbal and written communication skills including the ability to interact effectively with colleagues of varying technical and non-technical abilities.

- Passionate about agile software processes, data-driven development, reliability, and systematic experimentation.

- Passion for learning new technologies and solving challenging problems.

- Good understanding of CI, CD, TDD, and tools such as Jenkins.

- Strong understanding of orchestration frameworks such Airflow/Kubeflow/MLFlow.

- Agile software development experience such as Kanban and Scrum.

- Experience in software version control team practices and tools such as GIT and TFS.

- Expertise in Data Transformation and Manipulation through Big-Query/SQL

- Professional experience with Vertex AI and GCP Services.

- Strong proficiency in Python.

- 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.

Must have technical skills and experience :

Good to have skills :

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

Expert level Intermediate Level Basic Level

- ML Ops framework

- Big Query/SQL

- Python / R

- Vertex AI and GCP Services

- Docker-Container

- ML Orchestrator Kubeflow/Airflow

- GitHub

- Strong communication skills

- Machine Learning and Deep Learning algorithms

- Agile techniques

- Demonstrates team work skills.

- Mentor others and lead best practices.

- Understanding of ML Architecture

- Consumer Packaged goods domain knowledge

- Large Language Models and deployment architecture

- Graph database

- Tools like Neptune.

AI/ML Flow

- Feature Store

)

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