Bangalore, Karnataka, India Post Date: September 18, 2023 Full Time
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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.
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.