Job Title:  Enabling Areas - Information Technology - Deputy Manager - AI Solutions

 The team

The Enabling Areas – Information Technology team is responsible for building & maintaining different applications for Deloitte that focuses on providing employee experience.

Your work profile

Key Responsibilities

• Lead stakeholder interviews, workshops, and process walkthroughs to elicit and document functional/non-functional requirements (BRDs, user stories, acceptance criteria).

• Perform gap analysis, feasibility assessments, and traceability from business goals to product features.

• Shape product vision and roadmap for AI features; prioritize backlogs based on value, feasibility, and risk.

• Run Agile ceremonies (refinement, sprint planning, reviews) and maintain clear release plans and OKRs.

• Translate use cases into LLM/GenAI solutions (e.g., retrieval-augmented generation, prompt engineering, evaluation criteria) with DS/ML engineers.

• Coordinate data availability, model monitoring, and governance (security, privacy, cost/performance) with platform teams.

• Demo increments, validate requirements, and manage UAT with business users; drive adoption and training assets (playbooks, guides).

• Communicate status, risks, and mitigation plans to leadership; support post-launch analysis and continuous improvement.

• Maintain clear artefacts (journey maps, process flows, personas, wireframes) and QA/test plans in partnership with QA leads.

Key skills required

Skills (Must-have)

• Strong BA toolkit (elicitation, analysis, documentation, UAT).

• Product tools (Jira/ADO/Confluence), and proficiency in MS Office for analysis and communication.

• AI literacy: familiarity with LLMs/GenAI concepts, RAG patterns, evaluation/guardrails, and data governance.

• Collaboration across Data Science, Engineering, UX; excellent stakeholder communication.

Preferred Exposure

• Hands-on with prompt design, GenAI evaluation, vector databases, or model lifecycle (training, deployment, monitoring).

• Experience in cloud platforms (AWS/Azure/GCP) and modern data/ML tooling.

Success Metrics (KPIs)

• Requirement lead time (from intake to signed-off user stories), backlog health, and sprint goal attainment.

• Feature adoption (usage, satisfaction), model performance & cost (for AI features), and defect escape rate/UAT pass-rate.

Qualifications

•Master's or Ph.D. degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.

•Minimum of 5+ years of proven experience as a Data Scientist with a focus on NLP and 2 years on Gen AI, preferably in industry or research settings.

•Strong knowledge of NLP techniques and algorithms, including text preprocessing, feature extraction, and supervised and unsupervised learning methods.

•Proficiency in programming languages such as Python or R, and libraries such as NLTK, spaCy, scikit-learn, and TensorFlow/PyTorch for NLP.

•Familiarity with data visualization tools such as Matplotlib, Seaborn, or Plotly.

•Experience with containerization and orchestration technologies such as Docker and Kubernetes.

•Basic understanding of system design and ML-Ops practices.

•Excellent problem-solving skills and analytical thinking.

•Strong communication and collaboration skills, with the ability to work effectively in cross-functional teams.

•Proven ability to manage multiple projects simultaneously and meet deadlines in a fast-paced environment.