Machine Learning Engineer

Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation and navigate constant change. Through a combination of strategy, expertise and creativity, we help clients accelerate operational, digital and cultural transformation, enabling the change they need to own their future.

Join our team as the expert you are now and create your future.

We're seeking a Machine Learning Engineer to join the Data Science & Machine Learning team in our Commercial Digital practice, where you'll design, build, and deploy intelligent systems that solve complex business problems across Financial Services, Manufacturing, Energy & Utilities, and other commercial industries.

This isn't a research role or a support function—you'll own the full ML solution lifecycle from problem definition through production deployment. You'll work on systems that matter: forecasting models that inform multi-million-dollar decisions, agentic AI systems that automate complex workflows, and operational ML solutions that transform how enterprises run. Our clients are Fortune 500 companies looking for partners who can deliver, not just advise.

The variety is real. In your first year, you might build an agentic demand forecasting system for a global manufacturer, deploy an intelligent knowledge processing pipeline for a financial services firm, and architect an energy grid demand simulation model for a utilities company. If you thrive on learning new domains quickly and shipping intelligent production systems, this role is for you.

What You'll Do

Design and build end-to-end ML solutions—from data pipelines and feature engineering through model training, evaluation, and production deployment. You own the outcome, not just a piece of it.
Develop both traditional ML and generative AI systems, including supervised/unsupervised learning, time-series forecasting, NLP, LLM applications, RAG architectures, and agent-based systems using frameworks like Agent Framework, LangChain, LangGraph, or similar.
Build financial and operational models that drive business decisions—demand forecasting, pricing optimization, risk scoring, anomaly detection, and process automation for commercial enterprises.
Create production-grade APIs and services (FastAPI, Flask, or similar) that integrate ML capabilities into client systems and workflows.
Implement MLOps practices—CI/CD pipelines, model versioning, monitoring, drift detection, and automated retraining to ensure solutions remain reliable in production.
Collaborate directly with clients to understand business problems, translate requirements into technical solutions, and communicate results to both technical and executive audiences.

Required Qualifications

2+ (3+ years for Sr. Associate) years of hands-on experience building and deploying ML solutions in production—not just notebooks and prototypes. You've trained models, put them into production, and maintained them.
Strong Python and JavaScript programming skills with deep experience in the ML ecosystem (NumPy, Pandas, Scikit-learn, PyTorch or TensorFlow, etc.) and proficiency with JavaScript web app development.
Solid foundation in ML fundamentals: supervised and unsupervised learning, model evaluation, feature engineering, hyperparameter tuning, and understanding of when different approaches are appropriate.
Experience with cloud ML platforms, particularly Azure Machine Learning, with working knowledge of AWS SageMaker or Google AI Platform. We're platform-flexible but Microsoft-preferred.
Proficiency with data platforms: SQL, Snowflake, Databricks, or similar. You're comfortable working with large datasets and building data pipelines.
Experience with LLMs and generative AI: prompt engineering, fine-tuning, embeddings, RAG systems, or agent frameworks. You understand both the capabilities and limitations.
Ability to communicate technical concepts to non-technical stakeholders and work effectively with cross-functional teams.
Bachelor's degree in Computer Science, Engineering, Mathematics, Physics, or related quantitative field (or equivalent practical experience).
Flexibility to work in a hybrid model with periodic travel to client sites as needed.

Preferred Qualifications

Experience in Financial Services, Manufacturing, or Energy & Utilities industries.
Background in forecasting, optimization, or financial modeling applications.
Experience with deep learning frameworks such as PyTorch, Tensorflow, fastai, DeepSpeed, etc.
Experience with MLOps tools such as MLflow and Weights & Biases
Contributions to open-source projects or familiarity with open-source ML tools and frameworks.
Experience building agentic AI systems using Agent Framework (or predecessors), LangChain, LangGraph, CrewAI, or similar frameworks.
Cloud certifications (Azure AI Engineer, AWS ML Specialty, or Databricks ML Associate).
Consulting experience or demonstrated ability to work across multiple domains and adapt quickly to new problem spaces.
Master's degree or PhD in a quantitative field.

Why Huron

Variety that accelerates your growth. In consulting, you'll work across industries and problem types that would take a decade to encounter at a single company. Our Commercial segment spans Financial Services, Manufacturing, Energy & Utilities, and more—each engagement is a new domain to master and a new system to ship.

Impact you can measure. Our clients are Fortune 500 companies making significant investments in AI. The models you build will inform real decisions—production schedules, pricing strategies, risk assessments, capital allocation. You'll see your work drive outcomes.

A team that builds. Huron's Data Science & Machine Learning team is a close-knit group of practitioners, not just advisors. We write code, train models, and deploy systems. You'll work alongside engineers and data scientists who understand the craft and push each other to improve.

Investment in your development. We provide resources for continuous learning, conference attendance, and certification. As our DSML practice grows, there's significant opportunity to take on technical leadership and shape our capabilities.

Position Level

Associate

Country

United Kingdom

Details

Location

Belfast, UK

Experience

Min. 2 years2+ (3+ years for Sr. Associate) years of hands-on experience building and deploying ML solutions in production

Join our team as the expert you are now and create your future.

Machine Learning Engineer

What You'll Do

Design and build end-to-end ML solutions—from data pipelines and feature engineering through model training, evaluation, and production deployment. You own the outcome, not just a piece of it.
Develop both traditional ML and generative AI systems, including supervised/unsupervised learning, time-series forecasting, NLP, LLM applications, RAG architectures, and agent-based systems using frameworks like Agent Framework, LangChain, LangGraph, or similar.
Build financial and operational models that drive business decisions—demand forecasting, pricing optimization, risk scoring, anomaly detection, and process automation for commercial enterprises.
Create production-grade APIs and services (FastAPI, Flask, or similar) that integrate ML capabilities into client systems and workflows.
Implement MLOps practices—CI/CD pipelines, model versioning, monitoring, drift detection, and automated retraining to ensure solutions remain reliable in production.
Collaborate directly with clients to understand business problems, translate requirements into technical solutions, and communicate results to both technical and executive audiences.

Required Qualifications

2+ (3+ years for Sr. Associate) years of hands-on experience building and deploying ML solutions in production—not just notebooks and prototypes. You've trained models, put them into production, and maintained them.
Strong Python and JavaScript programming skills with deep experience in the ML ecosystem (NumPy, Pandas, Scikit-learn, PyTorch or TensorFlow, etc.) and proficiency with JavaScript web app development.
Solid foundation in ML fundamentals: supervised and unsupervised learning, model evaluation, feature engineering, hyperparameter tuning, and understanding of when different approaches are appropriate.
Experience with cloud ML platforms, particularly Azure Machine Learning, with working knowledge of AWS SageMaker or Google AI Platform. We're platform-flexible but Microsoft-preferred.
Proficiency with data platforms: SQL, Snowflake, Databricks, or similar. You're comfortable working with large datasets and building data pipelines.
Experience with LLMs and generative AI: prompt engineering, fine-tuning, embeddings, RAG systems, or agent frameworks. You understand both the capabilities and limitations.
Ability to communicate technical concepts to non-technical stakeholders and work effectively with cross-functional teams.
Bachelor's degree in Computer Science, Engineering, Mathematics, Physics, or related quantitative field (or equivalent practical experience).
Flexibility to work in a hybrid model with periodic travel to client sites as needed.

Preferred Qualifications

Experience in Financial Services, Manufacturing, or Energy & Utilities industries.
Background in forecasting, optimization, or financial modeling applications.
Experience with deep learning frameworks such as PyTorch, Tensorflow, fastai, DeepSpeed, etc.
Experience with MLOps tools such as MLflow and Weights & Biases
Contributions to open-source projects or familiarity with open-source ML tools and frameworks.
Experience building agentic AI systems using Agent Framework (or predecessors), LangChain, LangGraph, CrewAI, or similar frameworks.
Cloud certifications (Azure AI Engineer, AWS ML Specialty, or Databricks ML Associate).
Consulting experience or demonstrated ability to work across multiple domains and adapt quickly to new problem spaces.
Master's degree or PhD in a quantitative field.