Director, ML Engineering

Your Role Accountabilities

1.Leadership & Team Building

Lead, mentor, and grow a high-performing ML engineering team, fostering a culture of innovation and continuous improvement.

Define and execute the roadmap for building scalable, high-impact ML solutions that support the company's core business and strategic objectives .

Collaborate closely with leadership across departments, including data science, product management, and IT, to ensure alignment of ML initiatives with business needs.

Establish clear performance metrics and regularly assess the effectiveness of the team and individual contributors.

Promote knowledge-sharing, best practices, and a growth mindset within the team to enhance technical depth and execution efficiency.

2.End-to-End ML Solution Development

Oversee the design, development, deployment, and maintenance of machine learning models and systems that power content recommendation engines, personalization, automation, and other key business areas.

Drive the adoption of best practices in model development, testing, monitoring, and optimization across the team.

Build scalable, production-ready ML pipelines that process vast amounts of data and generate real-time insights.

Ensure that solutions are optimized for performance, cost-efficiency, and maintainability in a cloud-native, microservices environment.

Lead efforts to continuously improve model performance, incorporating user feedback and business metrics.

3.Innovation & Strategy

Identify and evaluate emerging technologies, algorithms, and methodologies in the AI/ML space, integrating them into the company's tech stack to maintain a competitive edge.

Work closely with senior leadership to define AI/ML strategies and influence decision-making on product and business development.

Evaluate and implement advanced techniques in natural language processing (NLP), computer vision, deep learning, reinforcement learning, and other domains as relevant to the media industry.

Provide thought leadership on the application of AI/ML in media and entertainment, positioning the company as a leader in AI-driven innovation.

4.Collaboration & Cross-Functional Engagement

Partner with product management, engineering, and business teams to translate complex business problems into technical ML solutions.

Collaborate on the integration of ML models into products and workflows, ensuring smooth end-to-end delivery from prototype to production.

Act as a trusted advisor to executives and stakeholders on ML capabilities, project status, risks, and business impact.

Drive the development and implementation of data governance, privacy, and security practices to ensure compliance with regulatory requirements.

Facilitate the sharing of ML insights with broader company teams, providing transparency and fostering a data-driven culture.

5.Performance Monitoring & Reporting

Define and track key performance indicators (KPIs) to measure the success of ML initiatives and models.

Oversee the collection and analysis of model performance data, providing regular updates to leadership and stakeholders.

Ensure that deployed models are continuously monitored , maintained, and updated to meet evolving business needs.

Lead post-mortem analyses of model failures and actively drive improvements based on lessons learned.

Utilize data to iterate and refine models to increase their accuracy and efficiency.

Qualifications & Experiences

Masters or Ph.D. degree in Computer Science, Engineering, Data Science, Machine Learning, or a related field from a reputed institution.

1 2 + years of experience in the field of machine learning and AI, with at least 5 years in a leadership or managerial role.

Proven track record of successfully leading and scaling ML engineering teams and delivering large-scale ML projects in a fast-paced environment.

Experience working in the Media & Entertainment industry or related sectors, with knowledge of data-driven content recommendations, personalization, and automation.

Expertise in designing, building, and deploying production-grade machine learning systems at scale.

Experience in leading cross-functional teams to deliver end-to-end machine learning solutions, from conceptualization to deployment and optimization.

Demonstrated ability to influence senior stakeholders and executives, translating technical concepts into business impact.

Strong expertise in machine learning algorithms, deep learning, reinforcement learning, and statistical modeling techniques.

In-depth knowledge of data structures, software engineering principles, and system design.

Experience with distributed computing and cloud technologies (AWS, GCP, Azure) and containerization (Docker, Kubernetes).

Proficiency in programming languages such as Python, Java, or Scala, and familiarity with ML frameworks like TensorFlow, PyTorch , or Keras .