Director, Product Manager - Agentic AI Platform

Who We Are

At Kyndryl, we design, build, manage and modernize the mission-critical technology systems that the world depends on every day. So why work at Kyndryl? We are always moving forward – always pushing ourselves to go further in our efforts to build a more equitable, inclusive world for our employees, our customers and our communities.

The Role

Kyndryl is at the forefront of AI innovation, building cutting-edge platforms that empower enterprises to leverage the full potential of artificial intelligence. We believe in creating intelligent, autonomous systems that redefine productivity and problem-solving. Join a dynamic team where your contributions directly shape the future of AI. We are looking for a skilled AI/ML Prompt Engineer to design, develop, and deploy advanced generative AI models that power a platform for creating business applications. You’ll work at the intersection of AI innovation and practical application, building intelligent systems that automate app generation, enhance user customization, and solve real-world business challenges. Collaborating with architects, developers, and product teams, you’ll bring cutting-edge AI capabilities to life in a scalable, production-ready environment.

Key Responsibilities:

Build, train, and fine-tune generative AI models (e.g., LLMs, GANs, VAEs) to support features like automated code generation, UI design, workflow creation, or data synthesis.

• Collaborate with the software architect to integrate AI models into the platform via APIs, ensuring seamless interaction with backend and frontend systems.
• Design and optimize data preprocessing pipelines to prepare high-quality datasets for training and inference, tailored to business application needs.
• Enhance model efficiency (e.g., latency, memory usage) for real-time or near-real-time application generation, using techniques like quantization or pruning.
• Research and experiment with state-of-the-art GenAI techniques to improve platform capabilities, such as natural language understanding, image generation, or predictive analytics.
• Rigorously evaluate model outputs for accuracy, relevance, and robustness, ensuring generated applications meet user expectations and industry standards.
• Work with DevOps to deploy AI models into production, leveraging cloud infrastructure (e.g., AWS, Azure, Google) and containerized environments (e.g., Kubernetes, EKS, AKS, GKE).
• Implement monitoring tools to track model performance and drift in production, iterating based on real-world feedback.

Kyndryl currently does not require employees to be fully vaccinated against COVID-19, however, if you are hired to work at a client, customer, or partner location, you may be required to show proof of vaccination to align with their respective COVID-19 vaccination policies.  Those who believe they are eligible may apply for a medical or religious accommodation prior to the start of employment.

Who You Are

You’re good at what you do and possess the required experience to prove it. However, equally as important – you have a growth mindset; keen to drive your own personal and professional development. You are customer-focused – someone who prioritizes customer success in their work. And finally, you’re open and borderless – naturally inclusive in how you work with others.

Required Skills and Experience

• 4+ years in AI/ML engineering, with at least 1-2 years focused on generative AI models or similar technologies.
• Strong proficiency in Python and AI/ML frameworks (e.g., TensorFlow, PyTorch, Hugging Face).
• Experience with model training, fine-tuning, and deployment in production environments.
• Familiarity with cloud AI platforms (e.g., AWS AgenticAI, Google AI Platform, Azure AI Foundry) and distributed computing.
• Knowledge of data engineering tools (e.g., Pandas, Spark) and version control (e.g., Git).
• Hands-on experience with generative models (e.g., GPT, Stable Diffusion, BERT) and their application in automation, content creation, or predictive tasks.
• Ability to tackle complex challenges, from dataset biases to model scalability, with creative and practical solutions.
• Capable of explaining AI concepts and trade-offs to technical and non-technical team members.
• Design, develop, and iterate on prompts to elicit optimal responses from AI models for various use cases.
• Analyze AI outputs to identify patterns, biases, or inconsistencies and adjust prompts accordingly.  
• Master’s degree or PHD in Computer Science, Data Science, Math