Director of Artificial Intelligence Engineering

Director of Engineering

Location: Bangalore ( Hybrid)

Full-time with Telus Digital

The Mission

As a Director of Engineering at Telus Digital, you will build and lead a high-performing team that delivers customer-loved products with exceptional quality, speed, and reliability. You will define

the technical vision and provide enterprise-level architectural guidance to ensure our platforms are scalable, secure, and seamlessly integrated with our business strategy.

What you will do

• Own Delivery & Reliability: Drive the end-to-end delivery of AI-powered products and features. Drive quarterly planning and execution, ensure commitments are met on time,

and uphold high availability, low latency, and deliver measurable business value.

• Define Enterprise Architecture: Partner with cross-functional leadership to define the enterprise-wide technical strategy for Telus Digital's AI initiatives. Establish architectural

standards, best practices, and governance frameworks to ensure consistency and quality across the organization.

• Lead future Leaders: Hire, coach, and develop a high-performing team of Engineers,Managers, and specialised technical talent. Foster a culture of technical excellence, accountability, and continuous learning to meet the evolving needs of the business.
• Champion Engineering Excellence: Drive a culture of quality, velocity, and continuous improvement. Set and maintain high standards for code health, test coverage, and developer experience to ensure sustainable development.
• Shape Strategy with Product: Partner with Product , Research and other critical stakeholder teams to translate strategic business goals into a focused roadmap. Make data-driven decisions
• Raise the Technical Bar: Guide architectural choices, unblock critical designs, and sponsor platform investments that reduce cycle time and improve the overall developer experience.
• Operational Excellence: Establish and enforce strong practices for on-call support, incident response, change management, and post-incident learning. Ensure that reliability and security are a shared responsibility for all team members.
• Scale the Organization : Design the team topology for a growing AI division. Define clear interfaces between research, data, engineering and all critical teams to ensure a fluid and efficient workflow.
• Budget and Vendor Stewardship : Own headcount plans and operating budgets.
• Evaluate buy vs. build decisions and manage relationships with strategic partners
• Strategic Agilit y: Be agile within an enterprise organization, and be ready to step up and evolve strategically to meet any evolving needs to grow the organization.

Qualifications:

• 12+ years in software engineering with 4+ years leading engineers and managers in multi-team organizations.
• Demonstrated experience in building, deploying, and managing complex, customer-facing systems preferably powered by Ml/AI at scale.
• Strong architectural judgment across a broad range of systems, data stores, API and modern full stack technologies, with a proven ability to design and govern large-scale systems. You have experience defining and socializing architectural standards.
• Strong technical background with the capability of being hands-on if required and earning the respect and ability to mentor top individual technical talent.
• Excellent people leadership skills, with a proven ability to set crisp goals, coach for performance, and build healthy, diverse teams.
• A data-driven approach to planning, trade-offs, and process improvement.
• Exceptional communication and interpersonal skills, with the ability to simplify complex concepts, negotiate effectively, and make clear decisions with executives and cross-functional partners.
• Fluency in delivery operations, including experience with on-call, incidents, and change management.
• Proven record of leading, mentoring, and scaling diverse teams of engineers from diverse top tier colleges and fortune top engineering companies
• Fluency with Data Engineering, Data Ops and Cloud practices, including data pipelines, feature stores, model training, versioning, and monitoring.

Nice to have:

• Direct experience with technologies like TensorFlow, PyTorch, Kubernetes, Kubeflow, or MLOps platforms.
• Experience with responsible AI practices and frameworks for fairness, privacy, and explainability.
• Hands-on background with one or more of our technologies: Java, Go, Python, Node, React, TypeScript, Kubernetes, Postgres, Kafka, Redis, BigQuery or Snowflake, Terraform.
• Practical exposure to ML or LLM-powered features, evaluation frameworks, and responsible AI practices.