Director – Machine Learning / AI

Zyoin Group; Bengaluru; On-site; INR 3,000,000 - 5,000,000; Full time

Job summary

A technology firm in Bengaluru is seeking a Director of Machine Learning to lead enterprise-scale AI and ML strategies. This role involves defining the ML vision, building high-performing teams, and ensuring the reliability of machine learning systems. The ideal candidate will have over 12 years of experience in AI/ML, including 4 years in leadership, and possess strong skills in Python, Generative AI, and NLP. This position offers a chance to innovate and push the boundaries of AI technology.

Qualifications

• 12+ years of experience in AI/ML with 4+ years in a leadership role.
• Proven experience building end-to-end ML systems in production environments.
• Excellent problem-solving, communication, and stakeholder management skills.

Responsibilities

• Define and own the ML/AI vision, strategy, and long-term roadmap.
• Lead, mentor, and develop high-performing machine learning and data science teams.
• Ensure high availability and reliability of ML systems.

Skills

Python

Machine Learning

Generative AI

NLP

Deep Learning

Graph Analytics

Education

Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, or a related field

Tools

AWS

Azure

GCP

Job description

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Experience: 12+ Years (4+ Years in Leadership)

Role Overview

We are seeking a visionary Director of Machine Learning to lead enterprise-scale AI and ML strategy, execution, and innovation. This role will drive the development of scalable, production-grade machine learning systems while building and mentoring high-performing teams across the organization.

Key Responsibilities

• Define and own the ML/AI vision, strategy, and long-term roadmap aligned with business objectives.
• Build and grow a strong pipeline of ML and AI leaders across teams.
• Lead, mentor, and develop high-performing machine learning and data science teams.
• Foster a continuous learning culture and stay ahead of industry trends and advancements.

Technical & Delivery Excellence

• Design, build, and deploy scalable, distributed machine learning systems across the full ML lifecycle.
• Develop advanced AI capabilities including Generative AI, Large Language Models (LLMs), NLP, Computer Vision, Voice Models, and Graph Analytics.
• Ensure technical solutions adhere to established standards for architecture, security, governance, and compliance.
• Oversee model lifecycle management including training, experimentation, serving, validation, and monitoring.

Cross-Functional Collaboration

• Partner with product, engineering, and business teams to identify opportunities for data-driven innovation and business growth.
• Collaborate with research organizations and external partners to explore emerging technologies.
• Translate complex technical concepts into actionable business outcomes.

Operational Excellence

• Ensure high availability and reliability of ML systems with strong focus on observability, monitoring, logging, and alerting.
• Implement and improve CI/CD pipelines and MLOps practices.
• Continuously enhance processes to improve delivery speed, scalability, and system performance.

Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, or a related field.
• 12+ years of experience in AI/ML with 4+ years in a leadership role.
• Proven experience building end-to-end ML systems in production environments.
• Strong proficiency in Python, Machine Learning, Generative AI, NLP, Deep Learning, and Graph Analytics.
• Experience working with cloud platforms (AWS, Azure, GCP) is an added advantage.
• Excellent problem-solving, communication, and stakeholder management skills.

What Sets You Apart

• Hands-on expertise operating ML solutions at scale across the entire ML workflow.
• Deep understanding of real-world ML systems including data pipelines, feature engineering, experimentation, and model deployment.
• Strong bias for engineering excellence, reliability, and service quality.
• Ability to think strategically while remaining hands-on with execution.