Description :

The Engineering Manager AI Products role provides ownership for delivery execution and people leadership for teams building LLM- and Vision AIenabled products on AWS.

This role is accountable for predictable delivery, production readiness, and operational ownership of AI products.

The Engineering Manager works closely with Product Management, Data Science, and AI Platform leadership to ensure AI initiatives are delivered reliably, securely, and at scale, while adhering to established architecture and platform standards.

Core Responsibilities :

AI Product Delivery (AWS-First) :

Own delivery for a portfolio of AI products and workflows, including :

- LLM-backed services and applications

- Vision AI pipelines (image validation, OCR, document processing)

- Ensure features progress from requirements to production deployment with defined readiness, monitoring, and support.

Own delivery sequencing across :

- Model integration

- Service and API development

- Deployment and promotion within AWS environments

Execution & Team Leadership :

- Lead teams of software engineers and ML engineers delivering AI products.

- Own team structure, role clarity, performance management, and career development.

Establish expectations for :

- Sprint execution and release cadence

- Production ownership and on-call participation

- Engineering quality and accountability

- Remove execution blockers related to resourcing, dependencies, or unclear ownership.

Model & Pipeline Execution :

- Ensure teams execute model-related work defined by Technical Leads, including :

- Integrating SageMaker-trained models into production services

- Running training, fine-tuning, and retraining workflows

- Orchestrating AI pipelines using AWS Step Functions

Track execution risks related to :

- Model readiness and availability

- Data dependencies

- Pipeline stability and operational gaps

- Ensure clean handoff from training, inference, monitoring.

Cross-Functional Coordination :

Act as the execution interface between :

- Product Management

- Data Science

- AI Platform and Technical Leads

- Security and Compliance

- Align delivery plans with shared AWS platform capabilities and constraints.

- Escalate delivery risks early when scope, dependencies, or timelines change.

Operational & Cost Accountability :

- Ensure teams operate within defined AWS cost and performance expectations.

- Track inference cost trends, training utilization, and AI-related production incidents.

- Drive corrective actions when delivery quality, reliability, or cost targets are missed.

Experience & Background :

- Extensive experience in software engineering with proven people leadership.

- Demonstrated delivery of AI- or ML-backed products into production.

- Experience managing multiple concurrent initiatives in an enterprise environment.

Strong execution background across :

- Agile delivery and dependency management

- Production readiness and operational ownership

Cross-team coordination :

Hands-on delivery experience on AWS, including :

- SageMaker (training, fine-tuning, inference)

- Step Functions (AI/ML workflow orchestration)

- Containerized and serverless deployments (ECS/EKS, Lambda, Docker)

- Practical understanding of cloud cost drivers, especially for model training and inference.