Director of AI Engineering; Microservices, LLM​/ML influencing

Position: Director of AI Engineering (Microservices, LLM/ML influencing)
Location: Bengaluru

Director of AI Engineering (Microservices, LLM/ML influencing)

Location:

Bangalore (Hybrid)
Full-time with Telus Digital

The Mission
As the Director of AI Engineering at Telus Digital, you will build and lead a high-performing team to deliver cutting-edge AI 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 tightly integrated with our business strategy.

What You Will Do
Own Delivery & Reliability:
Drive the end-to-end delivery of AI-powered products and features, ensuring high availability, low latency, and delivery of measurable business value.
Oversee quarterly planning and execution to ensure commitments are met on time.
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 organisation.
Lead Future Leaders:
Hire, coach, and develop high-performing teams of engineers, managers, and specialised technical talent.
Foster a culture of technical excellence, accountability, and continuous learning.
Champion Engineering Excellence:
Drive a culture of quality, velocity, and continuous improvement.
Set and maintain high standards for code health, test coverage, and the overall developer experience.
Shape Strategy with Product:
Collaborate with Product, Research, and other critical stakeholder teams to translate strategic business goals into a focused roadmap.
Make data-driven decisions and guide architectural choices.
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 shared responsibilities across the team.
Scale the Organisation:
Design team topology for a growing AI division.
Define clear interfaces between research, data, engineering, and other critical teams to ensure 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 Agility:
Adapt within an enterprise organisation, evolving strategically to meet the growing needs of the organisation.

Qualifications:

Required:

12+ years in hands-on software engineering with 4+ years leading engineers and managers in multi-team organisations.
Experience in building, deploying, and managing complex, customer-facing systems, preferably powered by  ML/AI e expertise in building scalable systems.
Strong architectural judgment across a broad range of systems, data stores, APIs, and modern full-stack technologies.
Experience in defining and socialising architectural standards.
Technical capability to be hands-on if required, and the ability to mentor top individual technical talent.
Excellent people leadership skills with the ability to set clear goals, coach for performance, and build diverse teams.
Data-driven approach to planning, trade-offs, and process improvement.
Exceptional communication and interpersonal skills.
Fluency in delivery operations, including experience with on-call, incidents, and change management.
Proven record of leading, mentoring, and scaling diverse engineering teams.
Fluency with Data Engineering, Data Ops, and Cloud practices, including data pipelines, feature stores, model training, versioning, and monitoring.
Preferred:

Experience with technologies like  Tensor Flow, PyTorch, Kubernetes, Kubeflow, or MLOps platforms.
Familiarity with responsible AI practices and frameworks for fairness, privacy, and explainability.
Hands-on experience with one or more of the following:  Java, Microservices, Go, Python, Node, React, Type Script, Kubernetes, Postgres, Kafka, Redis, Big Query, or Snowflake, LLM/ML influencing.
Practical exposure to ML or LLM-powered features, evaluation frameworks, and responsible AI practices.
What Success Looks Like:
First 90 Days:
Successfully integrate into the team.
Establish trust and credibility with the engineering team and key stakeholders.
Deliver initial AI solution components or quick wins.
Define a clear delivery roadmap and success metrics.
Year 1:
Deliver production AI systems driving measurable customer business outcomes.
Ensure TELUS Digital is viewed as an indispensable technical partner by customers.
Identify and scope additional opportunities within the customer organisations.
Contribute technical insights that influence TELUS Digital product/service offerings.
Ongoing:
Consistently deliver high-quality, production-ready AI solutions.
Maintain deep, trusted relationships with customer technical leadership.
Expand TELUS Digital’s footprint within customer organisations.
Serve as a technical authority and thought leader in the AI domain.
Mentor other engineers and share best practices.
Why This Role Matters:  In AI, implementation is…