VML - Lead Data Scientist - Deep Learning/Machine Learning

Description :

About the job :

Who We Are :

At VML, we are a beacon of innovation and growth in an ever-evolving world.

Our heritage is built upon a century of combined expertise, where creativity meets technology, and diverse perspectives ignite inspiration.

With the merger of VMLY&R and Wunderman Thompson, we have forged a new path as a growth partner that is part creative agency, part consultancy, and part technology powerhouse.

Our global family now encompasses over 30,000 employees across 150 offices in 64 markets, each contributing to a culture that values connection, belonging, and the power of differences.

Our expertise spans the entire customer journey, offering deep insights in communications, commerce, consultancy, CRM, CX, data, production, and technology.

We deliver end-to-end solutions that result in revolutionary work.

Lead Data Scientist

Permanent - India

The Opportunity :

We are investing massively in developing next-generation AI tools for multimodal datasets and a wide range of applications.

We are building large scale, enterprise grade solutions and serving these innovations to our clients and WPP agency partners.

As a member of our team, you will work alongside world-class talent in an environment that not only fosters innovation but also personal and professional growth.

You will be at the forefront of AI, leveraging multimodal datasets to build groundbreaking AI systems over a multi-year roadmap.

Your contributions will directly shape cutting-edge AI products and services that make a tangible impact for FTSE 100 clients.

We are hiring across all experience levels in AI engineering, from entry-level to seasoned professionals, and offer competitive salaries commensurate with experience and skills.

What Youll Be Doing :

- Design and deploy intelligent, agent-driven systems that autonomously solve complex, real-world problems using cutting-edge algorithms and AI libraries.

- Engineer collaborative agentic frameworks that coordinate multiple specialized agents to deliver advanced AI capabilities for enterprise-scale applications.

- Build and extend MCP-based infrastructure that enables secure, context-rich interaction between agents and external tools, empowering AI systems to act, reason, and adapt in real-time.

- Build human-in-the-loop agent workflows where humans benefit from AI assistance in decision-making and automation, while agents learn and improve through continuous human feedback.

- Stay abreast of the latest trends in AI research and integrate them into our products and services.

- Communicate technical challenges and solutions to non-technical stakeholders and team members.

What We Want From You :

- Fluency in Python and experience with core machine learning and deep learning frameworks (e.g., PyTorch, TensorFlow), with a proven ability to build, train, and evaluate models in real-world environments.

- Experience of managing a team

- Solid grasp of modern ML/AI fundamentals, including representation learning, optimization, generalization, and evaluation metrics across supervised, unsupervised, and generative settings.

- Experience working with multimodal data, including text, image, and structured formats, and building pipelines for feature extraction, embedding generation, and downstream model consumption.

- Hands-on experience integrating AI models into production workflows, including model inference, API deployment, and system monitoring.

- Proficiency in using version control, testing frameworks, and collaborative development workflows, including Git and basic CI/CD practices.

- Ability to communicate clearly about system behavior, trade-offs, and architectural decisions, especially when working across interdisciplinary teams.

- Understanding of LLMOps/MLOps principles, including model/version tracking, pipeline reproducibility, observability, and governance in production environments

If You Know Some Of This Even Better :

- Experience designing agentic systems, including goal-oriented multi-agent workflows, agent coordination strategies, and state/context management across long-running processes.

- Proficiency in agent orchestration frameworks such as LangChain, LangGraph, AutoGen, or custom frameworks designed to enable modular, reusable, and stateful agent components.

- Experience implementing secure agent-tool communication via infrastructures like Model Context Protocol (MCP), enabling agents to operate over filesystems, command-line tools, cloud APIs, and databases.

- Comfort with designing systems that evolve through human feedback, including methods like reinforcement learning from human feedback (RLHF), reward modeling, and in-context human correction.

- Competence in scalable deployment practices, including containerization (Docker), orchestration (Kubernetes), and cloud-native tools across AWS, GCP, or Azure