New opportunity

Director of Machine Learning

Onclusive · Navi Mumbai, India

About this role

Onclusive is seeking a Director of AI & ML Engineering to lead its AI and machine learning function. This hands-on leadership role involves defining AI/ML strategy, driving execution, and building production-grade systems that deliver measurable business value. The role requires a player-coach approach to lead cross-functional teams in developing innovative product improvements across the company's media analytics suite. Responsibilities include: Strategic Leadership: Define long-term AI/ML strategy aligned with product roadmaps, focusing on NLP, sentiment analysis, semantic search, and generative AI. Translate business problems into scoped products with clear success metrics. Technical and Delivery Leadership: Oversee the full ML lifecycle, including research, design, deployment, and MLOps practices. Establish architectural standards for data pipelines, embedding infrastructure, and agentic AI. Champion software engineering best practices and Agile delivery. People and Team Leadership: Mentor and develop a team of ML engineers, data scientists, and platform engineers. Scale the organization while maintaining a strong engineering culture. Cross-Functional Collaboration: Partner with product management, data platform, security, and compliance teams to ensure scalable, secure, and compliant AI systems. Requirements: 10+ years of experience in ML and AI engineering, with at least 2 years in leadership. Graduate studies in Computer Science, Applied Mathematics, Statistics, or equivalent industry experience (MPhil or PhD preferred). Deep technical expertise in NLP, computer vision, semantic search, and generative AI. Strong experience with MLOps, production ML serving infrastructure, and cloud platforms (AWS, GCP, or Azure). Advanced Python programming skills and proficiency with deep learning frameworks like PyTorch, TensorFlow, and HuggingFace. Experience with data pipeline tools such as dbt, Airflow, and Kafka. A strong product mindset is essential, with a preference for candidates with a background in product ownership.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Machine LearningArtificial IntelligenceNLPComputer VisionSemantic SearchInformation RetrievalAgentic AIGenerative AIMLOpsPythonTransformersPyTorchTensorFlowHuggingFacePEFTAWSGCPAzureKubernetesdbtAirflowKafkaSpark

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Strategic AI Product Alignment
The role requires translating complex business problems into scoped AI products. You must demonstrate the ability to align NLP, semantic search, and generative AI initiatives directly with product roadmaps and measurable business outcomes like accuracy, latency, and cost efficiency.
Production-Grade MLOps and Architecture
You must establish and evolve robust MLOps practices. This involves defining architectural standards for data pipelines, embedding infrastructure, and agentic AI, ensuring that research-led models are successfully deployed, reproducible, and reliable in a production environment.
Player-Coach Team Leadership
As a hands-on leader, you are expected to mentor a cross-functional team of ML engineers, data scientists, and platform engineers. You must balance individual technical contributions with the responsibility of scaling the organization and fostering a high-performance engineering culture.
Cross-Functional Governance and Compliance
You must partner with security and compliance teams to ensure all AI systems meet rigorous standards, including GDPR, the EU AI Act, and SOC2. This requires integrating compliance into the design phase rather than treating it as an afterthought.

Likely challenges to think through

Balancing Innovation with Product Stability
The primary challenge is maintaining a rapid pace of innovation in generative AI while ensuring the stability and reliability of existing media analytics products. Prepare by discussing how you prioritize technical debt against new feature development and how you manage stakeholder expectations regarding model performance.
Bridging Technical Depth and Product Ownership
The company specifically seeks a product-focused leader rather than a shared-services manager. You may face difficulty if your experience is purely academic or infrastructure-focused; prepare by highlighting specific instances where you owned the product lifecycle and user adoption metrics, not just the model accuracy.
Scaling Infrastructure for Diverse AI Modalities
Managing a stack that spans NLP, computer vision, and agentic AI requires complex infrastructure management. Prepare to discuss your experience with cloud-native scaling (Kubernetes, AWS/GCP/Azure) and how you design flexible data pipelines (Airflow, Kafka, dbt) that can support multiple, disparate AI use cases simultaneously.

Practice your answers

Technical practice

Designing Scalable Agentic AI Systems

What to coverDiscuss how you would architect an agentic AI system for media analytics. Focus on the trade-offs between latency, cost, and accuracy, and explain how you would manage state and tool-use reliability in a production environment.

MLOps Lifecycle and Reproducibility

What to coverExplain your approach to establishing MLOps standards for a team. Address how you ensure model reproducibility, automated testing, and monitoring for drift in production environments using tools like Airflow or Kubernetes.

NLP and Semantic Search Strategy

What to coverDescribe how you would implement a semantic search engine for large-scale media content. Discuss your choice of embedding models, vector database strategies, and how you would handle the challenges of domain-specific sentiment analysis.

Generative AI in Production

What to coverHow do you manage the risks associated with generative AI, such as hallucinations or data privacy, when integrating these models into a commercial product? Suggest methods for validation and guardrails.

Data Pipeline Architecture

What to coverGiven the need for real-time and batch processing, how would you design a data pipeline using tools like Kafka and dbt? Explain how you ensure data quality and lineage for downstream ML models.

Model Serving and Infrastructure

What to coverCompare the trade-offs of self-hosting models versus using managed cloud services on AWS or GCP. Discuss how you optimize for inference latency and cost-efficiency at scale.

Fine-Tuning and PEFT Strategies

What to coverWhen is it appropriate to use Parameter-Efficient Fine-Tuning (PEFT) versus full fine-tuning for large language models? Explain the impact on model performance and resource requirements.

Measuring AI Product Impact

What to coverHow do you define success metrics for an AI feature beyond technical accuracy? Explain how you align metrics like latency and cost with business-level KPIs such as user adoption or product value.

Behavioural practice

Balancing Technical Debt and Innovation

What to coverDescribe a situation where you had to balance the need for rapid product innovation with the requirement for robust, maintainable engineering standards. How did you prioritize your team's efforts?

Cross-Functional Stakeholder Management

What to coverProvide an example of how you have translated complex AI/ML concepts into actionable roadmaps for non-technical stakeholders like product managers or business leaders. How did you handle conflicting priorities?

Scaling and Mentoring Teams

What to coverShare an experience where you had to scale an ML team while maintaining a strong engineering culture. What specific actions did you take to mentor junior members and ensure high retention?

Navigating Compliance and Ethics

What to coverDescribe a time you had to ensure an AI system complied with strict regulatory requirements like GDPR or SOC2. How did you integrate these constraints into the development lifecycle without stifling delivery?

Sharpen your knowledge

Topics to prepare

AI/ML Strategic Alignment
Review how to map technical AI capabilities to business KPIs like latency, cost, and product impact. Exercise: Draft a hypothetical roadmap for a media analytics product that balances generative AI innovation with existing semantic search requirements, ensuring clear success metrics are defined for each phase.
MLOps and Production Governance
Focus on the lifecycle of production-grade systems, including reproducibility and compliance. Exercise: Design a high-level architecture for a secure, GDPR-compliant NLP pipeline that incorporates automated testing, CI/CD for models, and monitoring for drift in sentiment analysis outputs.

Skills to revise

Agentic AI and Generative Frameworks
Deepen proficiency in building agentic workflows using modern frameworks. Exercise: Build a prototype using HuggingFace and PyTorch that demonstrates an agentic approach to information retrieval, specifically focusing on how to handle multi-step reasoning tasks for media content analysis.
Data Pipeline Orchestration
Master the integration of data tools for scalable ML. Exercise: Create a workflow diagram using Airflow and dbt that demonstrates how to ingest raw media data, process it for embedding generation, and serve it via a Kafka stream for real-time analytics.
Cloud-Native ML Infrastructure
Focus on deploying models at scale using Kubernetes. Exercise: Review best practices for containerizing a deep learning model and deploying it on a cloud platform (AWS/GCP/Azure) with Kubernetes, focusing on auto-scaling and resource optimization for high-throughput inference.

Your preparation checklist

Prepare a case study on productionizing NLP or Generative AI
Develop a concise walkthrough of a past project where you took an AI model from research to production. Focus on how you defined success metrics (latency, cost, accuracy), managed the MLOps lifecycle, and ensured the solution delivered measurable business value.
Draft an AI strategy framework for media analytics
Create a brief proposal outlining how you would prioritize AI initiatives for a media analytics suite. Include your approach to balancing long-term R&D in semantic search or agentic AI with immediate product roadmap needs.
Outline your approach to MLOps and architectural standards
Prepare a summary of the architectural standards you would implement for scalable ML systems. Be ready to discuss your experience with cloud-native deployment (AWS/GCP/Azure), Kubernetes-based inference, and data pipeline tools like Airflow or dbt.
Formulate a team scaling and mentorship philosophy
Prepare examples of how you have mentored ML engineers and data scientists, managed cross-functional team dynamics, and maintained a high-performance engineering culture during periods of organizational growth.
Prepare strategic questions for leadership
Develop thoughtful questions regarding Onclusive's current data infrastructure, the primary challenges in their media analytics product suite, and how the AI team currently collaborates with security and compliance teams regarding regulations like the EU AI Act.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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