At ABB, we help industries run leaner and cleaner—and every person here makes that happen. You’ll be empowered to lead, supported to grow, and proud of the impact we create together. Join us and help run what runs the world.
This Position reports to:
Senior Operation Manager for D&A
Mission of the Role
Do you enjoy turning real business challenges into working AI solutions? Would you like to do it at a global company that is changing its industry with new technology? ABB is looking for a hands-on AI/ML Engineer to join our Information Systems (IS).
This is a specialist role where you build the solutions yourself. It is not a coordination or management role. You will work with the business to understand its needs and then find the best answer. That could be a predictive model, an LLM or SLM, an AI agent, a GenAI application, analytics, automation or a conventional solution. You will take that solution from idea to production and keep it running well in operations.
If you have experience with Azure AI/ML, Snowflake, GenAI and MLOps, and you want to own solutions from start to finish across both Dev and Ops, this opportunity is for you!
Work model: Hybrid #LI-Hybrid
Responsibilities
Business understanding and solution design
- Work with business stakeholders and Data Analysts to clarify needs, requirements, expected outcomes, success measures, data and context requirements, risks and ownership.
- Decide which approach fits each business problem best: AI/ML, GenAI, analytics, automation or a conventional solution. This includes choosing the right model type (ML, LLM, SLM), the right agent design and the right technology.
- Design scalable solutions that follow enterprise architecture, security and governance standards, with guidance from the Advanced Analytics Architect and platform architects.
Build and deploy
- Develop, validate and deploy production-ready AI/ML solutions using Python, SQL, Azure Machine Learning, Microsoft Foundry and other approved technologies.
- Build ML and GenAI applications, including predictive models, RAG, embeddings, vector search, prompt/context engineering, AI agents, APIs, orchestration, retrieval and enterprise data integrations.
- Build and integrate LLM-based solutions and conversational agents using Microsoft Foundry, Azure OpenAI and, where appropriate, Copilot Studio and Power Platform. Support Copilot and other AI platforms, such as Anthropic models.
- Use enterprise data from SAP and Salesforce to build predictive models, GenAI applications and AI agents that deliver measurable business value.
- Build secure, reliable data ingestion and processing from Snowflake and other enterprise sources, with attention to data quality, lineage, access control and cost efficiency.
- Apply Snowflake AI/ML capabilities, including Snowpark ML, Cortex AI, Cortex Search and AI Functions.
- Develop reusable components and automated pipelines for data preparation, feature engineering, training, inference, evaluation, testing, deployment and retraining, using CI/CD and version control.
Evaluate, operate and improve (DevOps / MLOps / AIOps)
- Evaluate model accuracy, explainability, grounding, agent behaviour, safety, business value, performance, reliability and cost.
- Set up and run MLOps/AIOps practices: monitoring, drift detection, cost monitoring, root-cause analysis, retraining and continuous improvement. You own both development and operations.
- Contribute to production readiness, operational handover, incident and problem analysis, monitoring design and reliable day-to-day operations.
- Apply Responsible AI, information security, privacy, compliance, identity and access management, secrets management and other ABB requirements within your solution scope.
Ownership and collaboration
- Own your deliverables end to end. Estimate effort, manage priorities, report progress and risks, and work independently within the agreed architecture and platform guardrails.
- Work closely with Data Analysts, Data Scientists, Snowflake Developers, Data Engineers, Power BI/Fabric Developers, platform specialists and domain experts.
- Document solution logic, assumptions, limitations, evaluation results and operational requirements. Contribute reusable assets, engineering practices, knowledge sharing and mentoring.
Requirements
Education and experience
- Master's degree in Computer Science, Data Science, Statistics, Applied Mathematics, Engineering or a related technical field, or equivalent practical experience.
- 4–6 years of hands-on experience in Data Science, AI/ML Engineering, analytics engineering or related roles, including taking AI/ML solutions to production.
- Experience delivering AI/ML solutions end to end: business requirements, data preparation, development, validation, deployment and monitoring.
Technical skills
- Practical Python and SQL skills for data preparation, model development, API integration, testing and implementation.
- Hands-on experience with Azure Machine Learning and Microsoft Foundry: pipelines, compute, environments, model deployment, endpoints, agents and evaluation.
- Strong understanding of GenAI patterns: LLMs, RAG, embeddings, vector search, prompt/context engineering, agent design and evaluation.
- Working knowledge of ML and statistics: supervised and unsupervised learning, model validation, regression, time series and hypothesis testing.
- Experience with ML frameworks such as Scikit-learn and, where relevant, XGBoost, PyTorch or TensorFlow.
- Experience with automated pipelines and CI/CD, using version control and testing practices, preferably in Azure DevOps or GitHub.
- Hands-on experience with Snowflake AI/ML tools (Snowpark ML, Cortex AI, Cortex Analyst, Cortex Search, AI Functions) and Snowflake connectors.
- Experience analysing, modelling and integrating SAP and Salesforce data. This means understanding source data structures, business entities, relationships and transformation requirements.
Operations and governance
- Understanding of how AI/ML solutions run in production: monitoring, reliability, performance, cost management, root-cause analysis and continuous improvement.
- Working knowledge of enterprise architecture, cloud security, privacy, compliance, data governance and Responsible AI principles.
Communication and language
- Ability to turn business needs into technical solutions, explain options and trade-offs, and communicate clearly with technical and business stakeholders.
- Fluent spoken and written English (minimum C1).
Nice to Have / Differentiators
- Experience with LLM frameworks such as the Azure OpenAI SDK, LangChain, Semantic Kernel or equivalent.
- Experience with other AI platforms and models (e.g., Anthropic, SLMs).
- Microsoft certifications: Azure AI Engineer Associate (AI-102), Azure AI Apps and Agents Developer Associate (AI-103), Azure Data Scientist Associate (DP-100) or Machine Learning Operations Engineer Associate (AI-300).
- Snowflake certifications, such as SnowPro Core or relevant GenAI certifications.
- Experience using Azure DevOps for work planning, execution and reporting.
- Experience with Copilot Studio, Power Platform, Microsoft Fabric, Power BI, Streamlit or other data visualisation tools.
- Knowledge of Azure infrastructure for AI/ML workloads: networking, VNets/private endpoints, Entra ID, RBAC, managed identities, Key Vault, storage, containers, Azure Monitor and Application Insights.
- Infrastructure as Code experience (Bicep or Terraform).
- Experience mentoring colleagues or contributing to shared engineering standards and reusable assets.
What We Offer
- The chance to be part of a global company working on high-impact industrial projects.
- A collaborative environment with continuous learning, advanced technologies and a strong development culture.
- Room to grow on a solid technical career path at ABB.
Building a cleaner, smarter future takes all kinds of minds: the curious, the courageous, and the creative. We welcome people from all backgrounds and experiences.
Ready to make an impact? Apply today or visit www.abb.com to learn more about the impact of our solutions across the globe.
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