Senior Technical Manager (US)

Job Summary

Key Responsibilities

• Manage the end-to-end AI project portfolio, including discovery, feasibility assessment, planning, development, deployment, and production support.
• Partner with business stakeholders to identify and prioritize AI use cases, define success criteria, and establish delivery roadmaps.
• Lead cross-functional teams comprising AI/ML engineers, application developers, architects, data engineers, QA, and platform teams.
• Provide technical direction and review solution architecture, integrations, engineering practices, and production readiness.
• Oversee initiatives involving Generative AI, enterprise assistants, agentic workflows, machine learning, and intelligent automation.
• Manage project dependencies, resource capacity, budgets, risks, and delivery commitments across concurrent initiatives.
• Establish AI governance covering data privacy, security, access controls, responsible AI, model evaluation, and human oversight.
• Ensure solutions meet agreed expectations for accuracy, reliability, performance, scalability, and operating cost.
• Collaborate with enterprise architecture, security, data, infrastructure, and operations teams to support implementation and adoption.
• Present portfolio status, business outcomes, risks, and decisions requiring leadership attention through regular governance reviews.
• Mentor technical leads, resolve delivery escalations, and promote reusable components and consistent engineering standards.
• Required Experience and Skills
• Significant experience in enterprise technology delivery, with demonstrated leadership of multiple projects and technical teams.
• Proven experience delivering AI/ML or Generative AI solutions into production.
• Strong understanding of LLMs, retrieval-augmented generation (RAG), embeddings, vector databases, prompt engineering, agent orchestration, and evaluation frameworks.
• Experience with enterprise AI platforms such as OpenAI services, Azure AI, Google Cloud Vertex AI, or AWS Bedrock.
• Strong knowledge of APIs, microservices, enterprise integrations, cloud architecture, containers, and Kubernetes.
• Familiarity with Python-based AI development, CI/CD, MLOps/LLMOps, observability, and model lifecycle management.
• Ability to assess architecture and technical trade-offs, challenge estimates, and guide teams through complex delivery issues.
• Strong stakeholder management, executive communication, negotiation, and team leadership skills.
• Ability to translate business needs into delivery plans and explain technical decisions clearly to nontechnical stakeholders.
• Job Title: Senior Technical Manager – Enterprise AI
• Role Overview
• We are seeking a Senior Technical Manager to lead the client’s Enterprise AI portfolio across multiple projects and teams. The role will own technical delivery, guide solution architecture, and work closely with business stakeholders and senior leadership to translate business priorities into secure, scalable AI solutions with measurable outcomes.

Preferred Qualifications

• Experience managing an enterprise AI portfolio or establishing an AI Centre of Excellence.
• Experience with enterprise knowledge integration, AI security guardrails, and organizational adoption.
• Exposure to vendor management, commercial planning, and cloud cost optimization.
• Bachelor’s or master’s degree in Computer Science, Engineering, or a related discipline.
• Success Measures
• Predictable delivery across the AI portfolio, measurable business value, stakeholder adoption, reliable production performance, and effective governance of AI risks and costs.
• Skill Requirements
• Job Title: Senior Technical Manager – Enterprise AI
• Role Overview
• We are seeking a Senior Technical Manager to lead the client’s Enterprise AI portfolio across multiple projects and teams. The role will own technical delivery, guide solution architecture, and work closely with business stakeholders and senior leadership to translate business priorities into secure, scalable AI solutions with measurable outcomes.
• Other Requirements
• Key Responsibilities
• Manage the end-to-end AI project portfolio, including discovery, feasibility assessment, planning, development, deployment, and production support.
• Partner with business stakeholders to identify and prioritize AI use cases, define success criteria, and establish delivery roadmaps.
• Lead cross-functional teams comprising AI/ML engineers, application developers, architects, data engineers, QA, and platform teams.
• Provide technical direction and review solution architecture, integrations, engineering practices, and production readiness.
• Oversee initiatives involving Generative AI, enterprise assistants, agentic workflows, machine learning, and intelligent automation.
• Manage project dependencies, resource capacity, budgets, risks, and delivery commitments across concurrent initiatives.
• Establish AI governance covering data privacy, security, access controls, responsible AI, model evaluation, and human oversight.
• Ensure solutions meet agreed expectations for accuracy, reliability, performance, scalability, and operating cost.
• Collaborate with enterprise architecture, security, data, infrastructure, and operations teams to support implementation and adoption.
• Present portfolio status, business outcomes, risks, and decisions requiring leadership attention through regular governance reviews.
• Mentor technical leads, resolve delivery escalations, and promote reusable components and consistent engineering standards.
• Required Experience and Skills
• Significant experience in enterprise technology delivery, with demonstrated leadership of multiple projects and technical teams.
• Proven experience delivering AI/ML or Generative AI solutions into production.
• Strong understanding of LLMs, retrieval-augmented generation (RAG), embeddings, vector databases, prompt engineering, agent orchestration, and evaluation frameworks.
• Experience with enterprise AI platforms such as OpenAI services, Azure AI, Google Cloud Vertex AI, or AWS Bedrock.
• Strong knowledge of APIs, microservices, enterprise integrations, cloud architecture, containers, and Kubernetes.
• Familiarity with Python-based AI development, CI/CD, MLOps/LLMOps, observability, and model lifecycle management.
• Ability to assess architecture and technical trade-offs, challenge estimates, and guide teams through complex delivery issues.
• Strong stakeholder management, executive communication, negotiation, and team leadership skills.
• Ability to translate business needs into delivery plans and explain technical decisions clearly to nontechnical stakeholders.