Join our dynamic Enabling Areas – Information Technology team as a Deputy Manager focusing on AI Solutions. This role is integral to building and maintaining applications that enhance the employee experience at Deloitte. You will be instrumental in shaping the future of AI within the organization, driving innovation and ensuring seamless integration of cutting-edge technologies.

This is an exciting opportunity to lead AI initiatives and collaborate with cross-functional teams to deliver impactful solutions. If you are passionate about AI and possess strong leadership and technical skills, we encourage you to apply.

Lead stakeholder engagements, including interviews and workshops, to meticulously document functional and non-functional requirements. Conduct thorough gap analyses and feasibility assessments, ensuring alignment from business objectives to product features.

Define the product vision and roadmap for AI features, prioritizing backlogs based on value, feasibility, and risk. Facilitate Agile ceremonies and maintain clear release plans and objectives.

Translate complex use cases into effective LLM/GenAI solutions, collaborating closely with Data Science and ML engineers. Oversee data availability, model monitoring, and governance to ensure security, privacy, and performance.

Demonstrate product increments, validate requirements with business users, and manage User Acceptance Testing (UAT). Develop adoption strategies and training materials such as playbooks and guides.

Provide regular status updates, communicate risks and mitigation plans to leadership, and support post-launch analysis for continuous improvement. Maintain essential project artifacts and collaborate with QA leads on test plans.

A Master's or Ph.D. degree in Computer Science, Engineering, Mathematics, Statistics, or a related discipline is required. A minimum of 5 years of experience as a Data Scientist, with a specialization in NLP and at least 2 years in GenAI, is essential.

Possess a strong understanding of NLP techniques and algorithms, including text preprocessing, feature extraction, and various learning methods. Proficiency in programming languages like Python or R, along with libraries such as NLTK, spaCy, scikit-learn, and TensorFlow/PyTorch, is critical.

Familiarity with data visualization tools (Matplotlib, Seaborn, Plotly) and experience with containerization technologies (Docker, Kubernetes) are necessary. A basic grasp of system design and ML-Ops practices is also expected.

Exceptional problem-solving and analytical skills are paramount. Strong communication and collaboration abilities are vital for effective teamwork. The capacity to manage multiple projects and meet deadlines in a fast-paced setting is a key requirement.