Job Summary

We are looking for a hands-on, techno-functional AI Manager to lead an established multidisciplinary team of AI Engineers, AI Architects, Machine Learning/Data Scientists, and Optimization Scientists. The individual will be responsible for providing technical and people leadership to a team of at least 10 professionals, maintaining oversight of critical AI initiatives, and ensuring that projects are delivered with the required quality, business impact, timelines, and architectural standards. This is not a coordination-only management role.

The successful candidate must be technically credible, capable of reviewing complex AI solution designs, and comfortable engaging with both business stakeholders and engineering teams.

Responsibilities

- Team Leadership: Lead and manage an established team of 10 or more AI Engineers, AI Architects, ML/Data Scientists, and Optimization Scientists.

- Team Leadership: Provide technical direction, coaching, performance management, and career development for team members.

- Team Leadership: Establish clear ownership, delivery expectations, and engineering standards across the team.

- Team Leadership: Promote collaboration across AI engineering, data science, architecture, optimization, cloud, platform, and business teams.

- Team Leadership: Identify capability gaps and support the continuous development of the team.

- Project and Delivery Oversight: Maintain close oversight of critical and high-impact AI projects from solution definition through production deployment.

- Project and Delivery Oversight: Regularly review project status, technical risks, dependencies, resource constraints, and delivery commitments.

- Project and Delivery Oversight: Proactively identify projects that are at risk and work with project leads to define corrective actions.

- Project and Delivery Oversight: Ensure that AI initiatives deliver measurable business outcomes and are not limited to prototypes or proof-of-concept implementations.

- Project and Delivery Oversight: Communicate delivery status, risks, decisions, and escalations clearly to senior stakeholders.

- Project and Delivery Oversight: Balance priorities and resources across multiple concurrent AI, ML, GenAI, and optimization initiatives.

- Technical and Architectural Leadership: Provide hands-on technical guidance for complex AI, machine learning, optimization, and Generative AI solutions.

- Technical and Architectural Leadership: Review and challenge solution architectures, technical designs, implementation approaches, and technology selections.

- Technical and Architectural Leadership: Architect scalable, secure, cost-effective, and production-ready AI solutions across Microsoft Azure and/or AWS.





- Technical and Architectural Leadership: Ensure appropriate integration between AI solutions and enterprise applications, APIs, data platforms, cloud services, and operational systems.

- Technical and Architectural Leadership: Guide teams on software engineering practices, including modular design, testing, version control, CI/CD, observability, reliability, and maintainability.

- Technical and Architectural Leadership: Ensure solutions meet enterprise requirements for security, privacy, governance, compliance, performance, and responsible AI.

- Generative and Agentic AI: Lead the design and implementation of enterprise Generative AI and Agentic AI solutions.

- Generative and Agentic AI: Guide teams on areas such as Retrieval-Augmented Generation, tool-calling agents, multi-agent workflows, prompt engineering, model routing, and AI orchestration.

- Generative and Agentic AI: Establish appropriate evaluation frameworks for GenAI and Agentic AI solutions, including accuracy, groundedness, relevance, safety, latency, reliability, and cost.

- Generative and Agentic AI: Ensure that GenAI applications include appropriate guardrails, human oversight, monitoring, and fallback mechanisms.

- Generative and Agentic AI: Evaluate emerging AI technologies and determine their suitability for enterprise use cases.

- Business and Stakeholder Engagement: Translate complex business problems into clear AI, ML, optimization, and data-driven solution approaches.

- Business and Stakeholder Engagement: Work with business leaders to define use cases, expected outcomes, success measures, and adoption plans.

- Business and Stakeholder Engagement: Communicate technical concepts, architectural decisions, trade-offs, risks, and limitations to both technical and non-technical stakeholders.

- Business and Stakeholder Engagement: Ensure alignment between business priorities, technical feasibility, delivery capacity, and enterprise strategy.

- Business and Stakeholder Engagement: Support the adoption and operationalization of AI solutions across the organization.

Mandatory Experience and Qualifications

- Minimum of 10 years of overall professional experience.

- Approximately 3 or more years of software development or software engineering experience.

- Approximately 57 years of experience in Data Science, Machine Learning, Artificial Intelligence, or Optimization.





- Demonstrated experience directly leading and managing a team of at least 10 professionals.

- Experience managing multidisciplinary teams that may include AI Engineers, AI Architects, ML Engineers, Data Scientists, and Optimization Scientists.

- Strong hands-on understanding of software engineering, solution architecture, cloud platforms, data systems, and production AI delivery.

- Proven experience taking AI or ML solutions from experimentation through production deployment and ongoing operations.

- Experience overseeing multiple critical projects, managing delivery risks, and ensuring commitments are met.

- Strong architecture experience across Microsoft Azure, AWS, or comparable enterprise cloud platforms.

- Experience designing solutions involving APIs, microservices, cloud-native applications, data pipelines, model deployment, monitoring, and MLOps.

- Practical experience with Generative AI, Large Language Models, Retrieval-Augmented Generation, or Agentic AI solutions.

- Understanding of GenAI and Agentic AI evaluation approaches, including quality, groundedness, safety, reliability, latency, and cost evaluation.

- Robust stakeholder management, communication, decision-making, and problem-solving skills.

Preferred Experience

- Experience delivering AI solutions within manufacturing, industrial, engineering, supply-chain, operations, or similar environments.

- Experience with optimization problems such as production planning, scheduling, inventory optimization, logistics, network optimization, or resource allocation.

- Familiarity with Azure AI Foundry, Azure Machine Learning, AWS SageMaker, Amazon Bedrock, Databricks, Kubernetes, and related AI platforms.

- Experience implementing AI governance, responsible AI, model risk management, and enterprise evaluation frameworks.

- Experience managing globally distributed or cross-functional teams.

- Experience working with senior business and technology leadership.

Candidate Profile

- The ideal candidate is a strong people leader who remains technically engaged. They should be able to move comfortably between reviewing an AI architecture, challenging a delivery plan, coaching a technical lead, resolving a project risk, and explaining the business value of an AI initiative to senior leadership.

- The role requires someone who can lead an already-established team, strengthen execution, maintain visibility over critical initiatives, and ensure that AI solutions are technically robust, production-ready, and aligned with business priorities.

Competencies

- Values: Integrity, Accountability, Inclusion, Innovation, Teamwork

- SET : Strategy, Execution, Talent (for managers)

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