TRCD-26-05469

AI Platform / MLOps Lead (On-Prem Kubernetes, Vendor Governance)

Senior contract role to own and govern an on-prem/sovereign AI platform and the end-to-end AI deployment pipeline (Dev→UAT→Prod) in a regulated banking environment. The person will run Kubernetes/containerized AI workloads including GPU orchestration, establish reusable “golden path” deployment patterns, and ensure production AI is observable, auditable, and compliant with data residency and security requirements. They will act as the technical counterpart to AI vendors/implementation partners—reviewing architectures, enforcing delivery standards, validating container images/models/integrations, and coordinating closely with InfoSec and Infrastructure for scanning, provisioning, and governance.

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Position summary

Location: India
Workplace: Remote
Employment: Contract
Experience: Minimum 10 years and Maximum 15 years

Apply now

Role overview

Why This Role Matters.

Your Impact

Deliver Enterprise Value

Help organisations solve complex business problems through modern technology, consulting expertise and measurable outcomes.

Collaboration

Work Across Teams

Collaborate with consultants, architects, engineers and client stakeholders throughout the project lifecycle.

Growth

Learn Continuously

Gain exposure to enterprise technologies, certifications, mentoring and real-world project experience.

Career Path

Grow With Ubique

Build a long-term consulting career with opportunities to take on greater responsibility and leadership over time.

Responsibilities

What You'll Be Doing.

Every role at Ubique contributes directly to solving meaningful business challenges for our clients.

Key responsibilities Deployment & platform

 Own the AI deployment pipeline (Dev → UAT → Production) and the sandbox

environment, ensuring a clear, governed, repeatable path to production.

 Operate and govern the on-prem / sovereign AI platform (Kubernetes,

containerisation, GPU orchestration) — configuring and managing it, including partner-provided components.

 Establish a reusable "golden path" deployment pattern so each successive use case ships faster than the last.

 Act as the technical counterpart to AI vendors and the implementation partner

review their architectures, challenge unrealistic timelines, and hold them to delivery commitments.

 Validate that vendor-delivered solutions (container images, models, integrations) meet the bank's security and residency requirements before deployment.

 Set the technical standards every vendor must deliver to, so solutions arrive in a form our environment can actually run.

 Work directly with Information Security (ISO) to enable and run container image

vulnerability and malware scanning (IVA/MSB) for AI workloads.

Technology stack

Tools & Technologies.

The platforms and technologies you'll use to build modern, enterprise-grade solutions.

Kubernetes

Required

Docker

Required

Containers

Required

Containerisation

Required

GPU orchestration

Required

On-prem

Required

Sovereign cloud

Required

In-region deployment

Required

Data residency

Required

MLOps

Required

Model registry

Required

Model versioning

Required

Model monitoring

Required

Drift detection

Required

Dev

Required

UAT

Required

Production

Required

CI/CD

Required

Container image scanning

Required

Vulnerability scanning

Required

Requirements

Skills & Experience.

We value curiosity, collaboration and continuous learning. If you don't meet every requirement but believe you can make an impact, we'd still love to hear from you.

Essential

Required Qualifications

10+ years in platform engineering / DevOps / MLOps / AI engineering with production deployments

End-to-end deployment pipeline ownership (Dev → UAT → Production) and governed release process

Hands-on Kubernetes administration/operations in production

Docker and containerisation in production

Container security in production (image vulnerability/malware scanning)

On-prem / sovereign / in-region deployment experience (not cloud-only); strong data residency compliance

GPU orchestration for AI workloads (platform operation/governance)

Vendor/partner technical management: architecture review, standards enforcement, delivery validation

Integration of third-party AI products into enterprise systems and data

Ability to review vendor architectures and identify feasibility gaps/risks

MLOps governance: model registry, versioning, monitoring, drift detection

Working with Information Security and IT Infrastructure teams to enable secure operations

AI project management experience (project manager responsibilities)

Preferred

Nice to Have

GPU infrastructure sizing and cost optimisation

Feature stores

Data pipelines / data foundation support

Regulated financial services experience (explicitly banking preferred)

Telco or government regulated environment experience

GCC / Arabic-region experience

What you'll gain

More Than Just A Job.

We're committed to helping every team member grow professionally, personally and technically while working on meaningful projects.

Global Exposure

Collaborate with international clients and multicultural teams on enterprise programmes.

Continuous Learning

Expand your expertise through mentoring, certifications and hands-on project experience.

Career Growth

Take ownership, develop leadership skills and grow your consulting career over time.

Flexible Working

Hybrid and remote collaboration designed around trust and delivering exceptional outcomes.

People First

Join a supportive culture where collaboration, respect and long-term relationships come first.

Enterprise Projects

Work on meaningful technology initiatives for leading organisations across industries.

Apply

Apply for AI Platform / MLOps Lead (On-Prem Kubernetes, Vendor Governance)

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