Manager - DS-AI & Machine Learning

Publicis Sapient is looking for an experienced Manager, Machine Learning Engineering to lead our talented team of AI and data science experts. In this influential role, you will be responsible for developing and implementing solutions that address complex business challenges across a wide range of industries. Your expertise will empower clients to revolutionize their businesses by harnessing the potential of advanced technology. As a Manager, Machine Learning Engineering, you will collaborate with cross-functional teams to strategize, develop, and deliver machine learning models tailored to meet specific business objectives. You will be responsible for overseeing the entire lifecycle of these models, from data preprocessing and algorithm selection to performance evaluation and seamless integration into production systems.

Your Impact:

• As a Manager Data Science specialising in Generative AI, you will:

  • Lead AI-Driven Innovations: Drive the development of state-of-the-art AI and machine learning solutions that transform business strategies and deliver exceptional customer experiences.
  • Strategic Collaboration: Work closely with cross-functional teams, including product managers, data engineers, and business stakeholders, to define and execute data-driven solutions aligned with organizational goals.
  • Foster a High-Performance Team: Build, mentor, and lead a team of talented data scientists, cultivating a culture of innovation, collaboration, and continuous learning.
  • Deliver Business Impact: Translate complex business problems into AI/ML solutions by leveraging advanced techniques such as generative AI, deep learning, and NLP, ensuring measurable outcomes.
  • Optimize AI Pipelines: Oversee the development and deployment of scalable, efficient, and robust machine learning pipelines that address latency, responsiveness, and real-time data processing challenges.
  • Customize AI Models: Direct the customization and fine-tuning of AI models, including large language models (LLMs) and other generative AI technologies, to meet domain-specific requirements.
  • Promote Data-Driven Decision-Making: Advocate for data-centric approaches across teams, ensuring data quality, integrity, and readiness to maximize model performance and business impact.
  • Develop Intelligent AI Agents: Architect and refine AI agents that solve complex business challenges, leveraging LLMs to deliver personalized, user-centric solutions.
  • Advance Generative AI Applications: Innovate with cutting-edge generative AI models such as LLM, VLM, GANs, and VAEs to create tailored applications for dynamic content creation, predictive analytics, and enhanced automation.
  • Scale AI with Cloud Technology: Deploy and scale LLM-based solutions on platforms like GCP, AWS  and Azure to address real-world business problems with precision and efficiency.
  • Stay at the Cutting Edge: Keep up-to-date with emerging trends and innovations in AI and data science, identifying opportunities to incorporate the latest advancements into projects.

8 years to 15 years with at least 2 years of Gen AI experience

8 years to 15 years with at least 2 years of Gen AI experience

Qualification: 8-14 Years with 2 Yrs Gen AI

Your Skills & Experience:

• Fine tuning experience of Large Language models (LLMs, VLLMs, or Vision model)
• Distributed training or inference experience with frameworks like Ray, vllm, openllm, bentoML etc.
• Experience with frameworks like like LangChain, Llamaindex for building maintainable, scalable Generative AI applications.
• Deployment experience or optimized hosting experience of Large Language models (LLMs, VLLMs, or Vision model)
• Experience working with any Vector database like Milvus, FAISS, ChromaDB etc
• Experience developing agents with frameworks like LangGraph, CrewAI, Autogen etc
• Experience with prompt engineering
• Keeping up with latest market trends
• Experience working with open source large language models from HuggingFace
• Experience working with atleast one public cloud provider such as Azure, AWS or GCP
• Experience working with container technology like Docker, ECS etc
• Experience with DevOps practices and CI/CD pipelines for data solutions
• Experience in deploying solutions to production with Kubernetes or OpenShift
• Experience with managing ML workflows with MLFlow or KubeFlow.