AI Solutions Technical Engineering Manager

You will lead multidisciplinary engineering teams (AI/ML, data, software, platform) to deliver AI capabilities that are trusted, adopted, and production-grade . You’ll partner with senior stakeholders & Product Managers to define outcomes, guide technical direction, and ensure delivery is focused on value not AI experimentation for its own sake.

You do not need to code day-to-day, but you must be credible in technical decision-making and able to challenge designs constructively.

Own delivery of AI workloads and AI-enabled products end-to-end: from discovery through build, launch, and iteration

Convert loosely defined ideas into delivery plans, milestones, and measurable outcomes

Lead engineering execution across multiple teams, ensuring clear ownership boundaries, interfaces, and ways of working

Drive pragmatic technical direction across AI systems: LLM/ML deployment patterns, model lifecycle, monitoring, and iteration

Ensure production readiness: security, reliability, observability, governance, and cost awareness (FinOps mindset)

Unblock teams through active problem-solving, dependency management, and escalation when needed

Balance trade-offs across speed, quality, risk, and cost and communicate them clearly

Manage stakeholder expectations with calm, credible leadership; run steering conversations and provide transparent updates

Build a culture of continuous improvement and innovation, with disciplined delivery under strict timelines

AI solutions move from idea to production with demonstrable business value

Delivery stays outcome-focused despite technical uncertainty and complexity

AI capabilities are operationally viable: monitored, reliable, secure, and cost-controlled

Teams understand ownership and interfaces; dependencies don’t derail execution

Stakeholders trust progress, decisions, and trade-offs even under pressure

Proven experience leading engineering delivery for data-heavy, AI-enabled, or platform products

Strong understanding of modern AI concepts: ML systems, LLMs, data dependencies, evaluation, and operational risks

Track record managing complex deliveries across multiple teams and stakeholders

Comfortable operating in federated/domain-oriented environments with shared ownership

Excellent communication: able to align senior stakeholders and guide teams through ambiguity

Solid grasp of production engineering fundamentals: cloud, reliability, security, monitoring, CI/CD

(Not hands-on coding daily, but technically credible)

Cloud: AWS / Azure / GCP

AI/ML delivery: model deployment, MLOps/LLMOps, monitoring, iteration

Platform foundations: Kubernetes (EKS/AKS), CI/CD, GitOps concepts

Observability: metrics, logs, tracing; dashboards and alerting disciplines

Architecture: APIs, microservices, event-driven systems; data pipelines

Experience delivering AI in regulated or high-stakes environments (e.g., financial services)

Familiarity with AI governance, ethics, and emerging regulation (e.g., EU AI Act)

Exposure to LLM platforms (e.g., Bedrock / Foundry) and multi-tenant cost controls

Consulting/client-facing delivery leadership and workshop facilitation

We believe talent knows no boundaries. Our hiring process focuses solely on your skills, experience, and potential to contribute to our team. We welcome applicants from all backgrounds and evaluate each candidate based on merit, regardless of personal characteristics as the age, gender, origin, religion, sexual orientation, neurodiversity or disability.