Machine Learning Engineer
Role details
Job location
Tech stack
Job description
allocation, and delivery timelines. Ensure projects meet quality standards and client expectations while maintaining technical excellence. Design and architect end-to-end ML solutions-from data pipelines and feature engineering through model training, evaluation, and production deployment. Make key technical decisions and own the overall solution architecture. Lead development of 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. Establish MLOps best practices-define and implement CI/CD pipelines, model versioning, monitoring, drift detection, and automated retraining standards to ensure, Senior ML / AI Engineer (LLMs & Agentic AI) Location: Belfast (Hybrid, 2-days a week in office) Base pay range: Direct message the job poster from ViVA Tech Talent The Role: Join a high-impact AI/ML team building production-grade systems using Large Language Models, RAG,..., Location: Belfast, United KingdomThales people architect solutions that are relied upon to deliver operational advantage at every decisive moment throughout the mission. Defence and armed forces customers rely on us to deliver the full range of defensive systems for land,...
Requirements
solutions remain reliable in production. Serve as a trusted advisor to clients-build long-standing partnerships, understand business problems, translate requirements into technical solutions, and communicate results to both technical and executive audiences. Contribute to practice development-participate in business development activities, develop reusable assets and methodologies, and help shape the technical direction of Huron's DSML capabilities.# Required Qualifications 5+ 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 at scale. Experience leading and developing technical teams-including coaching, mentorship, code review, and performance management. Demonstrated ability to build high-performing teams and develop junior talent. 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 architecting 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. Excellent communication and client management skills-ability to communicate technical concepts to non-technical stakeholders, lead client meetings, and build trusted relationships with executive audiences. Bachelor's degree in Computer Science, Engineering, Mathematics, Physics, or related quantitative field (or equivalent practical experience). Willingness to travel approximately 30% 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 HuronVariety 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#J-18808-Ljbffr Similar jobs