Job Description

Responsibilities:

• Architect, implement, and manage robust distributed systems that enable efficient and reliable distribution and training of machine learning workloads, utilising cloud infrastructure resources.
• Optimise, deploy and develop machine learning models in a distributed system architecture, leveraging NimbleEdge's model orchestration and management platform
• Provide technical leadership and mentoring within the ML team.
• Work towards continuous improvement in the team's standard of execution.
• Reliably scale critical machine learning services through a combination of rigorous testing, experimental measurement and application of cutting-edge performance enhancing research.
• Deploy complex machine learning, deep learning, and natural language processing solutions in various domains, such as operations improvement, churn prediction, fraud detection, recommendation systems, and more, utilising NimbleEdge's capabilities.
• Collaborate with cross-functional teams to understand business needs and design ML solutions that align with company goals, leveraging NimbleEdge's unique capabilities
• Improve ML solution delivery time through optimising processes and workflows.

Requirements:

• Demonstrated expertise in optimising and scaling distributed systems for large-scale machine learning workloads
• Experience in developing and executing transformational end-to-end machine learning projects
• Strong proficiency in programming languages such as Python, Java, or Scala, with experience in building distributed systems using frameworks like Spark, Hadoop, or Kubernetes
• Extensive experience with machine learning libraries/frameworks such as TensorFlow, PyTorch, or scikit-learn
• Familiarity with containerisation technologies (Docker, Kubernetes) and orchestration tools
• Deep experience in working with data lakes, data warehouses, and purpose-built data stores to enable unified governance, security, and disaster recovery
• Minimum of 5 years of professional experience as a Machine Learning Engineer or in a similar role, with a focus on distributed systems and production-level ML deployments
• Solid understanding of cloud platforms (e. g., AWS, GCP, Azure) and experience deploying ML models on cloud-based infrastructure
• Bachelor's or Master's degree in Computer Science, Engineering, or related field
• Excellent problem-solving and troubleshooting skills, with a strong attention to detail
• Effective communication skills and ability to collaborate with cross-functional teams in an agile environment.