Remote Senior AI Platform & Delivery Lead - Contract opening

Covansis IT Services Llp; United States; Remote; USD 276,000 - 358,000; Part time

Job summary

Covansis IT Services Llp seeks an experienced AI deployment lead to own the end-to-end delivery of secure, in-country AI deployments. You will govern the on-prem AI platform, manage vendor relationships, and ensure rapid, auditable progress from development to production.

This role emphasizes data residency, security compliance, and cross-functional collaboration with banks and partners to accelerate validated AI into production while maintaining high governance standards.

Qualifications

• 10+ years in platform/DevOps/MLOps or AI engineering.
• Hands-on Kubernetes and container security in production.
• On-premise or sovereign deployment with data residency requirements.
• Experience managing AI vendors and third-party integrations.
• Ability to critique vendor architectures for feasibility.

Responsibilities

• Own the deployment pipeline across Dev/UAT/Production and sandbox environments.
• Operate and govern the on-prem/sovereign AI platform (Kubernetes, containerisation, GPU orchestration).
• Establish a reusable golden-path deployment pattern for faster future use cases.
• Act as technical counterpart to AI vendors and implementation partners; review architectures and deadlines.
• Ensure vendor-delivered solutions meet security and residency requirements before deployment.
• Set technical standards that vendors must meet for compatibility with our environment.
• Coordinate with Information Security to enable container image scanning and risk controls.
• Oversee integration of third-party AI products into bank systems and data.
• Stand up model registry, versioning, monitoring and drift detection for governance.

Skills

10+ years in platform/DevOps/MLOps

Kubernetes in production

Docker/containerisation in production

On-premises deployment experience

Vendor management of AI vendors

Ability to read vendor architectures

AI project management

Tools

Kubernetes

Docker

GPU orchestration

Job description

Role Details

• Employment Type: CONTRACT (6 to 12 months contract)
• Work Model: Remote

Must-have experience

• 10+ years in platform / DevOps / MLOps or AI engineering, with a track record of putting solutions into live production
• Hands-on Kubernetes, Docker / containerisation, and container security in production.
• On-premise / sovereign / in-region deployment experience not cloud-only. Data residency is non-negotiable.
• Proven management of software / AI vendors and integration of third-party systems ideally in a bank, telco or government.
• Able to read a vendor's architecture and identify what will not work strong enough technically not to be bluffed.
• Experienced in managing AI Projects, as a project manager

Purpose of the role

Own the path that takes AI from a delivered solution to a secured, monitored production system running in-country and make that path fast. With delivery partner-led, this role is the bank's technical owner of vendor delivery and deployment: it holds vendors to the bank's security, residency and quality standards, integrates their solutions into our environment, and ensures the bank is never dependent on a vendor's pace or claims.

This is the role that turns a blocked pipeline into a working one. Its single measure of success is deployment speed how quickly validated AI reaches production, safely.

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.

Vendor & delivery management

• 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.

Security & integration

• Work directly with Information Security (ISO) to enable and run container image vulnerability and malware scanning (IVA/MSB) for AI workloads.
• Work with IT Infrastructure to provision, scale and govern compute for AI workloads.
• Own the integration of third-party AI products into the bank's systems and data.

MLOps & governance

• Stand up and run model registry, versioning, monitoring and drift detection so models stay accountable after go-live, not just at delivery.
• Ensure all AI in production is observable, auditable and aligned to CBO and risk expectations