Lead Machine Learning Engineer
We are seeking an experienced Lead Machine Learning Engineer to drive the development and implementation of cutting-edge machine learning solutions for our client.
Key Responsibilities:
* Lead and drive machine learning projects from inception to production, building 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 the 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.
Your Profile
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 framework, Java, and Scala.
* Strong computer science fundamentals such as algorithms, data structures, multithreading.
* In-depth experience building solutions using public clouds such as AWS, GCP.
* Experience using ML platforms like SageMaker, H2O, DataRobot, etc.
* Strong knowledge of 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:
* Familiarity with Generative AI concepts (LLaMA, Dall-E, ChatGPT, Bard, etc.).
* 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.