Key Responsibilities Job DescriptionTechnical Ownership & Delivery
- * Own the AI dimension of assigned programs end to end, from requirement interpretation through architecture, build, integration, and go-live.
- Prioritize and sequence use-case roadmaps based on business value and delivery feasibility, and set realistic, achievable delivery plans.
- Act as the primary technical point of contact in stakeholder and working sessions, building and maintaining technical credibility and trust.
Solution Architecture
- * Architect the data foundation (ingestion, storage, quality) required to support prioritized AI use cases.
- Select and design the AI/ML approach for each use case, for example text classification, machine translation, retrieval and similarity search, LLM-based assistants or chatbots, forecasting, and decision-support scoring, based on business value and technical feasibility.
- Design integration points into core business systems so that AI outputs are production-ready and directly usable in existing workflows.
Hands-On Engineering & Delivery
- * Build priority models and pipelines directly, and direct any shared or contracted engineering support assigned to a program.
- Establish MLOps practices (versioning, CI/CD, monitoring) sized appropriately to each program's scale and delivery cadence.
- Validate delivered models against agreed success metrics and produce the evidence required for stakeholder sign-off at each milestone.
Governance, Ethics & Compliance
- * Ensure AI components align with responsible-AI principles and data governance and privacy requirements, with explainability and audit logging built in by design.
- Identify and escalate technical risks, gaps, and scope trade-offs promptly to relevant stakeholders and leadership.
Stakeholder Management
- * Translate business and policy needs into scoped, deliverable technical work.
- Communicate scope, timeline, and resourcing trade-offs clearly, and recommend additional resourcing where a workstream's scale calls for it.
Required Qualifications Job Requirements
- * 8 - 11 years of experience in AI/ML engineering and architecture.
- Demonstrated experience owning AI solution delivery end to end, from design through production, in a lead architect or principal engineering capacity.
- Working knowledge across a range of AI/ML technique areas, such as NLP and text classification, machine translation, retrieval or vector-based similarity search, LLM-based assistants, and forecasting.
- Practical experience designing and operating data pipelines (ETL/ELT, lakehouse-style environments) to support AI use cases.
- Strong stakeholder communication, scoping, and prioritization skills, with the ability to translate business needs into technical designs and manage delivery trade-offs.
- Experience in regulated, public-sector, or specialized-domain environments is an advantage.
- Bachelor's degree in Computer Science, Engineering, Data Science, or a related field; advanced degrees are a plus.
