Director - Machine Learning Engineer - Baringa Partners LLP : Job Details

Director - Data, AI, Solutions & Engineering About Baringa: We set out to build the world's most trusted consulting firm - creating lasting impact for clients and pioneering a positive, people-first way of working. We work with everyone from FTSE 100 names to bright new start-ups, in every sector. You'll find us collaborating shoulder-to-shoulder with our clients, from the big picture right down to the detail: helping them define their strategy, deliver complex change, spot the right commercial opportunities, manage risk, or bring their purpose and sustainability goals to life. Our clients love how we get to know what makes their organisations tick - slotting seamlessly into their teams and being proudly geeky about solving their challenges. As a Certified B Corporation, we've proven that we've built social and environmental good into every bit of what we do. We have hubs in Europe, the US, Asia and Australia, and we work all around the world - from a wind farm in Wyoming to a boardroom in Berlin. Find us wherever there's a challenge to be tackled and an impact to be made. Our Digital & AI practice is looking for an experienced Machine Learning Engineer to join the team as a Director. In Digital & AI, we are focused on delivering value-adding, sustainable data capabilities, aligned to our client's specific needs. This expertise is applied across clients in all our industry market sectors (Financial Services, Product & Services, Energy & Resources, Telecommunications & Media). This is a unique opportunity to join an exciting, rapidly growing team with the backing of a much larger, established business. What you will be doing You will be using your experience to help our clients solve their most important data challenges through project delivery and high-quality project execution. You will be responsible for performing QA across various Machine Learning projects, acting as the US AI Lead for Baringa. You would also be responsible for business development and supporting the growth of our team, helping them build the skills they need to solve our client's challenges. Typical engagements include:

• Defining and implementing Machine Learning projects over the full lifecycle, from conception to data preparation, model engineering, evaluation and deployment and finally model monitoring and maintenance
• Establishing and developing ML Ops frameworks and standards for clients and embedding within their infrastructure
• Working with clients to take them on the journey, upskilling along the way and ensuring they are kept in the loop and can take ownership after you roll off the project
• Performing maturity assessments across clients' Cloud / AI environments and recommending improvements
• Building ML strategy blueprints and advising clients on the different technology options (MLFlow vs. Sagemaker vs. W&B etc.)
• Translating business requirements (both functional and non-functional) into solutions, ensuring compliance with the organization's strategy, policies, and standards and in some cases, help customers to define new policies, principles, and standards
• Helping clients to identify risks and mitigations for their ML and DS programs, as well as transition from on-prem to modern cloud-based infrastructures (AWS, Azure, GCP)
• Working with clients in key areas of ML model governance, such as in defining principles including fairness, transparency, interpretability, and accountability Your skills and experience We are looking for people who can both advise our clients and get hands on in technical delivery to bring a solution to life. Below, we list some of the skills and qualifications we are looking for. Of course, we do not expect you to be an expert in all areas and we understand that experiences vary based on the background and years of experience.
  • 10+ years of experience in professional services/consulting
  • Passionate individual who is excited by problems within machine learning and can bring a good mix of technical delivery and core consulting skills in client engagements
  • Expertise in ML modelling, comfortable with a range of supervised and unsupervised ML algorithms
  • Interested in building AI applications, ranging from image recognition applications to large language model based chatbots
  • Ability to design and maintain ML pipelines and architectures on modern frameworks to meet functional business requirements
  • Hands on experience using one of 3 major cloud technologies (AWS, Azure, or GCP) in a production environment, as well as ML platforms (e.g., AWS Sagemaker, Azure Machine Learning Studio)
  • Knowledge and experience of deep learning frameworks (e.g., PyTorch, Tensorflow)
  • Proven ability to build ML model deployment pipelines in a scalable production environment
  • Experience with LLM implementations (e.g., Chat Assistants, RAG, Embeddings, Guardrails, etc.)
  • Competent in Python or one other modern programming languages
  • A master's degree in a technical discipline (computer science, engineering or another STEM discipline) would be helpful but is not essential for success
  • Be a 'lifelong learner' and can demonstrate a drive to always be learning and developing your skillsets and develop the skillsets of others around you #J-18808-Ljbffr