Lead Machine Learning Engineer - Talent Smart

Experience: 10-15 Years
Mode: WFO ( 5 Days ) Job Location: Bangalore Job Type: Full Time Minimum 5 years experience in Object Oriented Design and/or Functional Programming in Python. 10 - 15 years of experience Mature software engineering skills, such as source control versioning, requirement spec, architecture, and design review, testing methodologies, CI/CD, etc. Must have a disciplined, methodical, minimalist approach to designing and constructing layered software components that can be embedded within larger frameworks or applications. Experience implementing machine learning frameworks and libraries such as MLflow Experience with containers and container managements (docker, Kubernetes) Experience developing cloud first solutions using Microsoft Azure Services including building machine learning pipelines in Azure Machine Learning and/or Fabric, Hands-on experience in deploying machine learning pipelines with Azure Machine Learning SDK Working knowledge of mathematics (primarily linear algebra, probability, statistics), and algorithms. Proficient at orchestrating large-scale ML/DL jobs, leveraging big data tooling and modern container orchestration infrastructure, to tackle distributed training and massive parallel model executions on cloud infrastructure. Experience designing custom APIs for machine learning models for training and inference processes and designing, implementing, and delivering frameworks for MLOps. Experience with model lifecycle management and automation to support retraining and model monitoring Experience implementing and incorporating ML models on unstructured data using cognitive services and/or computer vision as part of AI solutions and workflows. History of working with large scale model optimization and hyperparameter tuning, applied to ML/DL models. Knowledge of enterprise SaaS complexities including security/access control, scalability, high availability, concurrency, online diagnoses, deployment, upgrade/migration, internationalization, and production support. Knowledge of data engineering and transformation tools and patterns such as Databricks, Spark, Azure Data Factory Ability to engage other technical experts at all organizational levels and assess opportunities to apply machine learning and analytics to improve business workflows and deliver information and insight to support business decisions. Ability to communicate in a clear, concise, and understandable manner both orally and in writing.

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