AI Engineering Lead

At Schneider Electric's Digital Technology Centres (DTCs), we are building a next-generation enterprise AI Delivery team and are seeking an experienced AI Engineering Lead who combines deep, hands-on AI engineering expertise with technical leadership and people-development capabilities. You will remain closely involved in architecture, solution design, engineering reviews, productionisation, and resolution of complex technical challenges while leading a team of AI/ML Engineers. The role is centred on building and operating secure, scalable enterprise AI solutions, setting engineering direction, and ensuring technical quality from discovery through production.

The ideal candidate will have 8-12 years of experience spanning software engineering, cloud engineering, AI/ML systems, and MLOps, including 2+ years of technical or people leadership. They should bring strong AWS-based AI engineering depth, sound architectural judgement, and the ability to guide engineers while personally contributing to critical designs, reviews, prototypes, and production-readiness decisions.

The candidate will have responsibilities across the following functions:

Technical Leadership and Architecture:

• Act as a hands-on technical lead for the architecture, design, implementation, and productionisation of scalable, secure, reliable, and cost-efficient AI/ML and Generative AI solutions on AWS.
• Contribute directly to solution design, technical spikes, reference implementations, code and design reviews, troubleshooting, performance optimisation, and production-readiness assessments.
• Define engineering standards, architecture principles, reusable components, development frameworks, and best practices for AI solution delivery.
• Design and guide the development of cloud-native applications, APIs, microservices, RAG solutions, agentic AI systems, and enterprise AI platforms.
• Drive MLOps and LLMOps adoption, including experimentation, model and prompt versioning, evaluation, deployment, monitoring, governance, observability, and lifecycle management.
• Establish and improve CI/CD, Infrastructure-as-Code, containerisation, automated testing, release management, and platform automation practices.
• Make and review key architectural decisions across model integration, data and feature pipelines, application patterns, deployment approaches, scalability, resilience, security, and cost.
• Diagnose complex engineering issues and provide practical technical direction during development, testing, deployment, and live operations.
• Ensure enterprise-grade security, privacy, reliability, responsible AI, and compliance across AI solutions.
• Evaluate emerging AI technologies and AWS capabilities, validate them through hands-on experimentation, and translate them into pragmatic engineering roadmaps and reusable patterns.

People Leadership and Talent Development:

• Lead, mentor, and develop a team of AI Engineers through regular coaching, technical guidance, and constructive feedback.
• Set clear goals and ownership, support performance and career-development discussions, and maintain accountability for delivery and engineering quality.
• Support workforce planning, interviewing, hiring, onboarding, and role-based capability development as the team scales.
• Foster a collaborative, inclusive, high-performing engineering culture built on learning, ownership, innovation, and continuous improvement.

Delivery and Stakeholder Management:

• Own the technical delivery of AI initiatives from discovery through production and ongoing operations.
• Partner with business, product, architecture, data, cloud, cybersecurity, and governance teams to align priorities and execute AI roadmaps.
• Manage capacity, dependencies, timelines, risks, quality, and delivery governance while removing blockers.
• Communicate progress and technical trade-offs, ensure measurable business value, and promote reuse of common AI capabilities.

Requirements:

• 8-12 years of experience in software engineering, cloud engineering, AI/ML engineering, data science, or related technology domains.
• 2+ years of experience in technical leadership or people leadership, with demonstrated ownership of architecture, engineering quality, delivery, and team development.
• Strong hands-on expertise in Python, APIs, object-oriented design, testing practices, code quality, and modern software engineering principles.
• Deep experience with AWS cloud services and cloud-native architecture, including Amazon Bedrock, Amazon SageMaker AI, AWS Lambda, Amazon ECS or EKS, Amazon S3, Amazon API Gateway, AWS Step Functions, Amazon CloudWatch, AWS IAM, and related services.
• Experience designing, building, and operating production-grade AI/ML and Generative AI systems on AWS.
• Strong knowledge of ML lifecycle management, model serving, feature and data pipelines, experiment tracking, model monitoring, drift detection, observability, and retraining patterns.
• Experience with LLM applications, prompt engineering, RAG, embeddings, vector databases, agentic AI patterns, tool integration, evaluation, guardrails, and LLM observability.
• Experience with MLflow, Databricks, Amazon SageMaker AI, or equivalent platforms for experimentation, model management, and deployment.
• Hands-on experience with Docker, Kubernetes or Amazon EKS, Terraform or AWS CloudFormation, and CI/CD using GitHub Actions, Jenkins, AWS CodePipeline, Azure DevOps, or equivalent tools.
• Strong understanding of distributed systems, microservices, event-driven architecture, security, identity and access management, networking, data protection, API management, and cost optimisation.
• Working knowledge of Azure or other cloud platforms is beneficial but not mandatory.

Consulting Experience:

• Proven track record in an IT consulting environment, engaging with large enterprises and MNCs in strategic data and AI solutioning projects.
• Strong stakeholder management, business-needs assessment, solution shaping, and change-management skills.

Leadership and Soft Skills:

• Proven ability to lead, manage, coach, and develop high-performing engineering teams.
• Ability to operate as a hands-on player-coach who stays close to architecture, design, code quality, productionisation, and complex technical problem-solving while guiding the team.
• Strong stakeholder management, executive communication, and the ability to translate complex AI topics into clear business implications.
• Experience driving engineering strategy, delivery governance, prioritisation, and organisational capability development.
• Demonstrated ability to influence decisions and align cross-functional teams without relying solely on formal authority.
• Strong problem-solving, decision-making, coaching, conflict-resolution, and team-building skills.
• Excellent written and verbal communication, presentation, facilitation, and mentoring capabilities.

Preferred Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related field.
• AWS certification such as AWS Certified Machine Learning Engineer - Associate, AWS Certified Solutions Architect - Professional, or a relevant AWS speciality or professional-level certification.
• Experience managing AI engineering teams in an enterprise product, shared-services, consulting, or global capability-centre environment.
• Experience establishing AI engineering standards, reusable accelerators, technical communities of practice, or platform adoption patterns.
• Experience with AI governance, responsible AI, security reviews, model risk, data privacy, and production-readiness assessments.

Good to Have:

• Technical leaders who enjoy building both systems and teams.
• Player-coaches who can provide architectural guidance while enabling engineers to grow and succeed.
• Strategic thinkers who connect business outcomes with practical AI technology investments.
• People leaders who foster collaboration, innovation, accountability, inclusion, and continuous improvement.
• Individuals who translate complex AI concepts into secure, scalable solutions and measurable value.
• Adaptable leaders who stay current with the rapidly evolving AI landscape while maintaining engineering discipline.

Skills

AWS, Bedrock, Databricks, Docker, EKS, LLMOps, Lambda, MLOps, MLflow, Python, SageMaker