Baringa
Consulting
MachineLearningEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Machine Learning Engineer at Baringa. Skills: Machine Learning, MLOps, Data science, AI solutions. Define machine learning projects. Implement machine learning projects”
Industry & Context.
Problem-solving skills
What They're Looking For.
Must Have
Advanced degree in computer science, Advanced degree in mathematics, Advanced degree in physics, Advanced degree in engineering, Advanced degree in STEM field, Problem-solving skills, Solid grounding in classical ML, Solid grounding in deep learning, Hands on experience in using one of 3 major cloud technologies, Hands on experience in ML platforms
Nice to Have
PhD preferred, Specific ML framework experience, Cloud platform certs
What You'll Do.
Define machine learning projects
Implement machine learning projects
Establish ML Ops frameworks
Develop ML Ops standards
Embed ML Ops within infrastructure
Perform maturity assessments
Recommend improvements
Build ML strategy blueprints
Advise clients on technology options
Translate business requirements
Ensure compliance with strategy
Define new philosophies
Identify risks for ML programmes
Identify risks for DS programmes
Identify mitigations for ML programmes
Identify mitigations for DS programmes
Transition from on-prem
Transition to cloud-based infrastructures
Define ML model governance
Define fairness philosophies
Define transparency philosophies
Define interpretability philosophies
Define accountability philosophies
Build production ML solutions
Get hands on technical delivery
Bring solution to life
Build AI applications
Build forecasting tools
Build image recognition applications
Build LLM-based chatbots
Build LLM-based agents
Build machine learning models
Build machine learning pipelines
Adhere to software engineering best practices
Encourage applications from minority groups
How You'll Work.
Team & Collaboration
Client-facing engagements; Cross-functional teams; Collaborative approach
Communication Scope
Client-facing; Technical delivery
Process & Methodology
ML Ops
Full Job Description
About Baringa Baringa is a global consulting firm that partners with leaders to drive change and create value. With deep industry expertise, and enabled by advanced technology, the firm helps clients to deliver with greater confidence and certainty. With over 2,000 people across the UK, Europe, North America, Asia and Australia, the firm combines global insight with local understanding. The firm works across energy and resources, financial services, government and public sector, consumer products and retail, pharmaceuticals and life sciences, manufacturing, and technology, media and telecoms, with capabilities spanning strategy, transformation and operational excellence – all powered by advanced technology, data, AI and digital innovation. Clients value Baringa’s collaborative approach and the way its teams integrate seamlessly – all working with a shared understanding of what matters most. The firm is known for its kind, curious experts who listen closely and care deeply about client success as they help clients transform energy markets, modernise financial platforms, expand telecoms and digital networks through advanced data analytics, enable digital services in government, and unlock growth in consumer sectors. Certified as a Great Place to Work around the world, Baringa has been recognised by the Financial Times in 22 categories of its UK Leading Management Consultants rankings, and by Forbes for four consecutive years as one of the World’s Best Management Consulting Firms. Our Solutions and AI Lab Team are looking for experienced Machine Learning Engineers to join the team. In SAIL, we build state-of-the-art AI solutions that help our clients with some of their biggest projects - ranging from tools that support energy networks forecast risk and adapt to climate change using empirically-derived resilience models, to image recognition software using satellite and aerial imagery, to genAI-powered applications including bespoke assistants and agents. We are focused
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