Job Description

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Role Summary:

The Director of AI Platform & MLOps is a critical, hands-on technology leader responsible for the architecture, execution, and day-to-day management of the infrastructure powering our AI Training Data Services. You will lead a world-class team of engineers to build, scale, and operate a robust, secure, and highly automated platform. This role requires a deep technical background in cloud infrastructure, MLOps, and large-scale systems, combined with proven experience in leading high-performing engineering teams.

Responsibilities

Technical Execution & Architecture:

Translate

the long-term vision into an executable technical roadmap, making key architectural decisions for the platform.

Lead the hands-on design and implementation

of our MLOps/LLMOPs framework, including CI/CD for models, data/model versioning, automated workflows, and monitoring.

Engineer and manage

a scalable, multi-cloud infrastructure (AWS/GCP/Azure) using Infrastructure as Code (IaC) principles (Terraform, CloudFormation).

Oversee

the technical integration, scaling, and reliability of data annotation platforms and the GIG worker technology layer.

Drive

SRE best practices to ensure high availability, performance, and security of the entire platform.

Team & Operational Leadership:

Recruit, lead, mentor, and directly manage

a team of Cloud, DevOps, MLOps, and Site Reliability Engineers.

Foster a culture of technical excellence

, automation, and accountability within the engineering team.

Manage

the day-to-day project timelines, resource allocation, and operational activities to ensure successful platform delivery.

Implement and optimize

the cloud budget (FinOps) for your domain, ensuring cost-effective scaling.

Stakeholder & Client Engagement:

Act as the primary technical expert

in project-level discussions with clients, providing detailed solutioning and estimation for new deals.

Collaborate

closely with product management and service delivery teams to ensure the platform meets their requirements.

Skills & Qualification

Experience:

12+ years in platform engineering or DevOps, with at least 4 years in a leadership role

directly managing engineering teams

.

Cloud Architecture Mastery:

Deep,

hands-on experience

designing and managing complex infrastructure on AWS, GCP, or Azure. Expertise with Kubernetes (EKS, GKE, AKS), serverless, and core cloud services.

Expertise in MLOps/LLMOPs:

Demonstrable,

in-depth implementation experience

with the full MLOps lifecycle (e.g., Kubeflow, MLflow, Seldon, DVC, Airflow) and infrastructure for LLMs (Vector DBs, fine-tuning environments).

Infrastructure as Code (IaC):

Strong, hands-on proficiency

with Terraform or CloudFormation is a must.

Technical Leadership:

Proven ability to lead, mentor, and scale a technical team, driving projects from conception to production. *

Problem Solving:

Exceptional ability to debug and solve complex technical issues in a distributed systems environment.