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About the Role
hackajob is collaborating with Baringa Partners to connect them with exceptional professionals for this role.
What you will be doing
- 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
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Your Skills And Experience
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We are seeking passionate and dynamic ML engineers who are excited by building production ML solutions, and keen to take an active part in the growth of the company. We’re looking for people who can both advise our clients and get hands on in technical delivery to bring a solution to life.
- 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)
- 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
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
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