Machine Learning Engineer

Job Description:

About the role

You will join a team of talented and friendly Data Scientists in Machine Learning Operation (MLOPs) and AI (Artificial Intelligence) as a Data Science Squad member.

Team develops state-of-the-art Machine Learning-powered services for automated Supply Chain optimisation, Pricing strategy improvements, and Service Lifecycle Management including Generative AI-powered Knowledge management and warranty claim fraud detection and more.

Team is performing full MLOps cycles from customer pain discovery to production including:

What would you do?

• Work closely with researchers on all the stages MLOps including DS-Ops (Research and spikes), Data-Ops (Data discovery, Feature selection and engineering) and Model-Ops (build, test and ship production-grade models).
• Work in cross-functional teams to research, build and deliver the most efficient AI/ML powered solutions for our client problems including Knowledge base, Supply Chain optimization and Fraud Detection.
• Research design, implement, and deploy machine learning models and algorithms that address specific challenge within after-market, supply chain and price domains.
• Collaborate with team-members and clients globally to understand project requirements, objectives, and constraints.
• Process and analyse datasets to extract meaningful insights and features for model development.
• Design, implement and maintain industry-standard MLOps infrastructure for new and existing ML products
• Optimize and standardize ML training and validation processes, data warehousing and pipelines.
• Add automation, drift detection, logging, version control and testing pipelines to the MLOps architecture.

Who you are? 

• Understanding standard Machine Learning algorithms (like decision trees, neural networks, clustering, and support vector machines).
• You have good knowledge and experience of Python.
• You have deep experience with scientific libraries and frameworks such as NumPy, TensorFlow, Kubeflow, Keras, Scikit Learn, OpenCV.
• Practical industry experience deploying and maintaining ML systems in production.
• Proficiency in programming languages, frameworks, and tools, such as Python, TensorFlow, PyTorch. Experience with data preprocessing, feature engineering, and model evaluation techniques.
• Experience working on Gen AI, LLM, knowledge base and supply chain related projects
• Experience deploying MLOps solutions and working within CI/CD frameworks
• Experience with Linux systems and cloud infrastructure (AWS, etc.)
• Experience developing embedded ML applications
• Technically curious about the emerging AI innovations.
• Capable of testing using frameworks such as PyTest.

The icing on the cake:

• Ideally an MSc or PhD in either one of Artificial Intelligence, Computer Science or Applied Mathematics.
• Experience working on Gen AI, LLM, knowledge base and supply chain related projects
• Knowledge of Kubernetes and Docker.
• Familiarity with different image and video data format.
• Familiar with designing, building and troubleshooting distributed and scalable systems.
• Experience in building and maintaining cloud-hosted services on AWS.
• Experience working with MLOps users and data scientists to analyse data,
• Ability to slice and dice big data with SQL using python.
• Experience working with Dag execution workflow engines like airflow, Kubeflow, etc.
• Experience with big data tools: Spark, EMR.
• Has done feature engineering with Python scientific libraries,
• Experience in running machine learning models in AWS cloud.
• Knows about model drift and data drift and how does it affect inferences.
• Managed to deliver scalable inferences.
• Understands platform thinking perspective for delivering machine learning use cases for product teams.