AI Engineer (Energy)

AI Engineer (Energy)

John Cockerill, enablers of opportunities

Driven since 1817 by the entrepreneurial spirit and thirst for innovation of its founder, the John Cockerill Group develops large-scale technological solutions to meet the needs of our time: facilitating access to 
low-carbon energies, enabling sustainable industrial production, preserving natural resources, contributing to greener mobility, enhancing security and installing essential infrastructures. 

Its offer to companies, governments and communities consists of services and associated equipment for the sectors of energy, defense, industry, environment, transport and infrastructures. 

With more than 8 200 employees, John Cockerill achieved a turnover of € 1.649 billion in 2025 in 27 countries, on 5 continents.

 Location - Mumbai, India

 

Job Purpose

John Cockerill is a global engineering group founded in 1817 and headquartered in Seraing, Belgium, delivering large-scale technological equipment across energy, defense, and industry. John Cockerill Energy is executing a 2026–2030 IT, digital and Applied-AI transformation across Cooling, Energy Solutions and Energy Transition, including a shift from experimental agents to industrialized, governed AI products.
Working under the direction of the AI Product Manager, the AI Engineer transforms approved use cases into production-grade AI solutions while ensuring compliance with corporate AI governance, cybersecurity requirements, approved technology standards, and scalability expectations. The role combines AI engineering, software engineering, cloud architecture, prompt engineering, integration development, and MLOps/LLMOps practices.

 

 

Key Responsibilities

 

AI Solution Development
Design and develop AI applications, copilots, and multi-agent systems aligned with approved business requirements. • Build AI-powered assistants using Microsoft Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Graph, Power Platform and Azure Services. • Develop reusable AI components and shared frameworks. • Translate user stories and functional requirements into working AI solutions.

• Agentic AI Engineering
Design and develop multi-agent systems using approved architecture. • Implement orchestration frameworks, agent collaboration patterns, tool calling, memory management, and human-in-the-loop controls. • Design agent interfaces, workflows, guardrails, and monitoring capabilities. • Participate in architecture reviews and technical governance.

• Prompt Engineering & Model Development
Create, test, optimize, and document prompts. • Evaluate AI model performance. • Develop Retrieval-Augmented Generation (RAG) solutions. • Implement vector search and semantic retrieval capabilities. • Optimize accuracy, latency, and cost.

• Data & Integration Engineering
Integrate AI products with enterprise systems including SAP S/4HANA, JD Edwards, Planview, SharePoint, Teams, Microsoft 365 and engineering platforms. • Design APIs and data pipelines. • Ensure secure handling of enterprise data.

• Cloud & Platform Engineering
Deploy solutions on approved cloud environments. • Implement CI/CD pipelines. • Configure
monitoring, logging, and alerting. • Support infrastructure automation and environment management.

• LLMOps / MLOps
Establish deployment pipelines for AI products. • Manage AI lifecycle activities: development, testing, validation, deployment, monitoring and retirement. • Monitor token consumption and cloud costs. • Implement model governance and version control.
• Security, Risk & Compliance
Ensure compliance with Group AI Policy, Energy AI Governance SOP, cybersecurity standards, data privacy requirements and EU AI Act requirements. • Participate in risk assessments and architecture reviews. • Support audit and compliance activities.

• Quality Assurance
Conduct model testing and validation. • Measure accuracy and business performance. • Execute AI evaluation frameworks. • Document known limitations and mitigation actions.
• Documentation & Knowledge Management

Maintain technical documentation. • Create architecture diagrams and deployment guides. • Produce support documentation and training materials. • Ensure solutions can be maintained independently of individual developers.

• Adoption & Continuous ImprovementSupport pilots, Proofs of Concept (PoCs), and production rollouts. • Troubleshoot production issues. • Analyze usage patterns and user feedback. • Recommend enhancements that improve business outcomes.

 

Qualifications & Skills

Education & Experience

•Bachelor's / master’s in engineering, Computer Science, Data Science, Artificial Intelligence, Software Engineering, Information Technology, or related discipline.
•3+ years of experience in software engineering, data engineering, AI engineering, or cloud solution development including 2+ years delivering AI/ML, GenAI, Copilot or advanced analytics solutions.
•Experience deploying enterprise solutions on Microsoft Azure.
•Experience working in global organizations and cross-functional teams.
•Energy, EPC, industrial manufacturing, engineering or capital-goods sector experience preferred.
Technical & Product Skills AI Engineering & Knowledge Systems
•Generative AI, LLMs and Agentic AI: design enterprise AI assistants and goal-oriented agents using foundation models, orchestration, tool integration, guardrails and human-in-the-loop controls.
•RAG, vector databases and knowledge engineering: ground AI outputs in trusted enterprise content using embeddings, semantic search, metadata, taxonomies and structured knowledge repositories.
•Prompt engineering and AI evaluation: design, test and document prompts; assess AI outputs for accuracy, relevance, safety, latency, cost and business value. Microsoft AI, Cloud & Integration Platform
•Azure AI Foundry and Azure OpenAI: prototype, evaluate, deploy and monitor governed AI applications using enterprise-managed models and approved Azure AI services.
•Copilot Studio, Microsoft 365 Copilot and Graph API: build and govern copilots connected to Microsoft 365, enterprise permissions, workflows and collaboration data.
•Power Platform, Azure Functions and Logic Apps: create low-code automations, serverless components and integrations across Microsoft 365, APIs and enterprise systems.
Software Engineering, DevOps & Operations
•Python, REST APIs and JSON: develop AI services, integration utilities, automation components and structured data interfaces for enterprise systems.
•GitHub, CI/CD and software design patterns: manage version control, code reviews, automated deployments and reusable architectures for maintainable AI products.
•Azure Cloud, infrastructure as code and observability: deploy, monitor and operate AI workloads using secure, repeatable and auditable cloud environments.
•Security by design and FinOps: embed access control, data protection, secure coding, cost monitoring and resource optimization from design through production.

 

4. Behavioral Competencies

•Strong analytical and problem-solving skills
•Innovation mindset with business focus
•Ability to translate business needs into technical solutions
•Structured and disciplined approach to engineering
•Strong communication skills with technical and non-technical stakeholders
•Ownership mentality and continuous improvement mindset
•Ability to work independently in a global matrix organization

5.Travel & Work Arrangement

This role requires periodic travel to Europe to work directly with business-line and Group IT stakeholders. The position is based in the Navi Mumbai office, with a maximum of 2 days’ work-from-home per week as per discussion with the reporting manager.

John Cockerill offers you career and development opportunities within its various sectors in a friendly working environment. 
Do you want to work for an innovative company that will allow you to take up technical challenges on a daily basis?
We look forward to receiving your application and to meeting you!
 

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Experience Level

Mid Level