Devops AI Engineer

ABOUT JMAN:

JMAN Group is a fast-growing data engineering & data science consultancy . We work primarily with Private Equity Funds and their Portfolio Companies to create commercial value using Data & Artificial Intelligence. In addition, we also work with growth businesses, large corporates, multinationals, and charities.

We are headquartered in London with Offices in New York, London and Chennai. Our team of over 450 people is a unique blend of individuals with skills across commercial consulting, data science and software engineering.

We were founded by cousins Anush Newman (Co-founder & CEO) and Leo Valan (Co-founder & CTO) and have grown rapidly since 2019. In May 2023 we took a minority investment from Baird Capital and in January 2024 we opened an office in New York with the ambition of growing our US business to be as large as, if not bigger than, our European business by 2027.

Why work at JMAN:

Our vision is to ensure JMAN Group is the passport to our team’s future. We want our team to go on a fast-paced, high-growth journey with us – when our people want to do something else, the skills, training, exposure, and values that JMAN has instilled in them should open doors all over the world.

Current Benefits:

− Competitive annual bonus

− Market-leading private health insurance

− Regular company socials

− Annual company away days

− Extensive training opportunities

Key Responsibilities:

• Build and operate cloud infrastructure on Azure to support application and data platform environments.
• Implement infrastructure using Infrastructure as Code (IaC) tools such as Terraform, ensuring consistent, reliable, and repeatable deployments.
• Design, build, and maintain CI/CD pipelines using tools such as Azure DevOps or GitHub Actions for automated build and deployment processes.
• Manage source control (Git) workflows, including branching strategies, versioning, and release management.
• Deploy and manage application and platform workloads on Azure, ensuring reliability, scalability, and availability.
• Deploy, configure, and manage AI platform environments such as Azure AI Foundry, Azure OpenAI, Azure Machine Learning, and related cloud-native AI services, supporting AI, analytics, and Generative AI workloads.
• Build and maintain automated deployment pipelines for AI/ML and Generative AI workloads, including environment configuration, access management, monitoring, scalability, governance, and operational reliability.
• Support the deployment, operationalisation, and lifecycle management of Large Language Model (LLM) based solutions, ensuring production readiness, platform reliability, security, and scalability.
• Exposure to configuring and managing modern data platform environments, including Azure Databricks and Microsoft Fabric, to support downstream AI and analytics workloads, is good to have.
• Collaborate directly with Data Science, AI Engineering, Analytics, and Software Engineering teams to enable secure deployment and operation of AI-enabled applications and services.
• Implement and manage containerised workloads using Docker and Kubernetes (AKS).
• Manage and optimise cloud infrastructure across environments, ensuring performance, cost efficiency, and reliability.
• Configure and maintain monitoring, logging, and alerting systems to ensure platform observability and proactive issue detection.
• Maintain clear documentation for infrastructure, pipelines, deployment processes, and platform standards.

Skills & Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent industry experience.
• 4+ years of experience in DevOps, Platform Engineering, or Cloud Infrastructure, with practical experience supporting AI/ML, Data, and Generative AI platforms.
• Strong proficiency in Microsoft Azure, including compute, networking, storage, security, and AI services, with experience deploying and managing cloud-native applications and AI-enabled solutions.
• Azure certification (AZ-104 or AZ-400 equivalent) are considered an added advantage and valued as evidence of practical cloud platform knowledge.
• Strong experience with Infrastructure as Code (Terraform preferred).
• Hands-on experience with CI/CD tools such as Azure DevOps, GitHub Actions, or similar.
• Strong understanding of Git-based workflows, including branching, version control, and release strategies.
• Demonstrated commitment to continuous learning through certifications, labs, technical communities, hackathons, or hands-on experimentation with modern cloud and AI technologies.
• Proficiency in scripting (PowerShell, Bash, or Python) specifically tailored for infrastructure and AI automation tasks.
• Strong problem-solving skills and ability to work independently.

Behavioural Competencies

At JMAN, we expect our team members to embody the following:

• Proactive and accountable in driving platform readiness
• Adaptable and comfortable with ambiguity
• Strong collaborator across engineering and consulting teams
• Committed to continuous improvement, learning, and technical upskilling
• Professional, reliable, and delivery-focused
• Curious and adaptable towards emerging technologies, including AI-assisted engineering and automation practices.