Production ML Engineer for AI Solutions

Baringa; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Baringa is seeking experienced Machine Learning Engineers to lead production ML solutions across diverse industries. You will define projects through conception to deployment, build ML pipelines, and collaborate with clients on governance and cloud transitions.

The role emphasizes hands-on delivery, client advisory, and continuous learning in a fast-growing AI practice within SAIL. Strong communication and collaboration are essential.

Qualifications

• Passionate person who is excited by problems within machine learning and can bring a good mix of technical delivery and core consulting skills in client engagements
• Advanced degree in computer science, mathematics, physics, engineering or related STEM field
• Strong problem-solving skills and solid grounding in classical ML and deep learning: from applied statistics and traditional machine learning algorithms to transformers and SOTA deep learning
• Excellent collaboration and communication skills, both with teams and in client-facing engagements
• Interested in building AI applications, ranging from forecasting tools to image recognition applications and LLM-based chatbots and agents
• Proven ability to build machine learning models and pipelines using Python and common ML and DL libraries (e.g. Pytorch, Tensorflow) from early conceptualisation to full deployment in scalable production environments
• Ability to design, deploy and maintain ML solutions on modern frameworks to meet functional business requirements, adhering to software engineering best practices and with exposure to version control, testing, MLOps, CI/CD and API design
• Hands on experience in 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)

Responsibilities

• 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
• Translating business requirements (both functional and non-functional) into solutions, ensuring compliance with the organisations strategy, policies and standards and in some cases, help customers to define new policies, philosophies and standards
• Helping clients to identify risks and mitigations for their ML and DS programmes, 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 philosophies including fairness, transparency, interpretability, and accountability

Skills

Machine learning

Python

ML Ops

Cloud computing

Education

Advanced degree in CS/Math/Physics/Engineering

Tools

PyTorch

TensorFlow

AWS/Azure/GCP

SageMaker/Azure ML

Job description

Baringa is seeking experienced Machine Learning Engineers to lead production ML solutions across diverse industries. You will define projects through conception to deployment, build ML pipelines, and collaborate with clients on governance and cloud transitions.

The role emphasizes hands-on delivery, client advisory, and continuous learning in a fast-growing AI practice within SAIL. Strong communication and collaboration are essential.