ML Engineer

Machine Learning Engineer As a Machine Learning Engineer, you will play a pivotal role in driving the development and implementation of cutting-edge machine learning solutions for our client. Your responsibilities will encompass a wide range of tasks, from leading a small team of machine learning engineers to collaborating with cross-functional teams to deliver impactful solutions. You will be at the forefront of driving innovation and leveraging the power of machine learning to solve real-world problems, drive business growth, and create value. Key responsibilities: • Lead and drive machine learning projects from inception to production: build relationships with business partners and cross-functional teams. • Collaborate with business leaders, subject matter experts, and decision-makers to develop success criteria and optimize new products, features, policies, and models. • Partner with data scientists to understand, implement, train, and design machine learning models. • Collaborate with the infrastructure team to improve the architecture, scalability, stability, and performance of ML platform. • Construct optimized data pipelines to feed machine learning models. • Extend existing machine learning libraries and frameworks. • Develop processes, model monitoring, and governance framework for successful ML model operationalization. • Define objectives for the Machine Learning platform, own the technical roadmap, and be accountable for delivering results. • Define standards for engineering and operational excellence for running best-in-class ML platforms and continue to improve ML platforms to keep up with the latest innovations. • Design and implement architectural best practices in the delivery of data science use cases. Key skills/knowledge/experience: • Extensive software engineering experience with a strong working experience as a Machine Learning Engineer. • Bachelor's degree in computer science, computer engineering, or a related engineering field. Master's degree preferred. • Advanced proficiency with Python, Java, and Scala. • Strong computer science fundamentals such as algorithms, data structures, multithreading. • Experience working with Generative AI, using LangChain for Gen AI and techniques like RAG. • Experience using ML and DL Libraries:XGBoost, SKlearn, Tensorflow or PyTorch • In-depth experience building solutions using public clouds such as AWS, GCP. • Experience using ML platforms like SageMaker, H2O, DataRobot, etc. • Strong knowledge on ML model development life cycle components like containers, batch vs real time inference endpoints, application security testing etc. • Experience managing relationships in a cross-functional environment with multiple stakeholders. • Experience with developing and deploying production-grade applications with ML inferences using automation pipeline on cloud. • Experience working in Agile/ Scrum development process. • Thought leadership and innovative thinking. • Excellent communication and collaboration skills. Good to have: • Search platform experience (Solr, Elasticsearch, etc.). • Experience in building end to end recommender systems. • Exposure to graph databases and platforms, e.g. Neo4j. • Exposure to CI/CD tools like Jenkins. • Financial Services, particularly Insurance and 401K domain knowledge. • AWS Solutions Architect certification.

Created: 2026-03-10