Machine Learning Specialist

2 weeks ago

Mumbai, India Career Stone Consultant Full time

Role: Machine Learning Lead

Location: Mumbai, Hybrid


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 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 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 teamwork

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 (GCP

Vertex Feature Store,

Feast etc)

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