Machine Learning Specialist

2 weeks ago

delhi, 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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