Machine Learning Engineer

Overview

In this role you will design and deliver end-to-end ML projects, from data prep to deployment and monitoring, within Baringa’s Solutions and AI Lab. You will establish ML Ops standards for clients and help them migrate to modern cloud infrastructures. You’ll collaborate with clients to upskill teams and embed governance and responsible AI practices. This is a hands-on consulting position with opportunities to shape client AI strategy and drive impact across industries.

Responsibilities

• Define and implement ML projects across the full lifecycle, including data prep, model engineering, evaluation, deployment and monitoring
• Establish ML Ops frameworks and standards for clients and embed them into client infrastructure
• Upskill clients during engagement and enable ownership after project handover
• Perform maturity assessments of clients’ Cloud/AI environments and propose improvements
• Build ML strategy blueprints and advise on technology options
• Translate business requirements into technical solutions aligned with client policies and standards
• Identify risks and mitigations for ML/DS programs and support cloud migration from on-prem to cloud (AWS, Azure, GCP)
• Contribute to ML model governance discussions on fairness, transparency, interpretability, and accountability

Key requirements

• Passionate and dynamic ML engineer with hands-on delivery and client engagement experience
• Advanced degree in a STEM field
• Strong problem-solving with grounding in classical ML and DL (statistics, ML algorithms, transformers)
• Excellent collaboration and client-facing communication skills
• Interest in building AI apps (forecasting, image recognition, LLM chatbots)
• Proven ability to build ML models and pipelines in Python using libraries like PyTorch or TensorFlow
• Design, deploy and maintain ML solutions with software engineering practices, including version control, testing, MLOps, CI/CD and API design
• Hands-on experience with at least one major cloud platform (AWS, Azure or GCP) and ML platforms (e.g., AWS SageMaker, Azure ML)
• Lifelong learner with a drive to develop skills and help others grow

• collaboration
• client-facing communication
• problem-solving
• Python
• PyTorch
• TensorFlow

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