Senior Machine Learning Engineer

$104,860 - $194,740/Yr

Manulife - Boston, MA

Full-time - Mid Level

Hybrid - Boston, MA

Insurance Carriers and Related Activities

About the position

The Senior Machine Learning Engineer will be part of the U.S. Advanced Analytics team, responsible for packaging models, automating workflows, and operationalizing analytics solutions to generate business value for the insurance division of JH/Manulife. This role will contribute to designing, recommending, and implementing platforms, infrastructure, and standard methodologies in MLOps/LLMOps to support data scientists. The position involves close collaboration with multi-functional teams, including Underwriting, Pricing, IT, Data Office, Operations, Sales, and Distribution. The ideal candidate will be an expert programmer with a strong understanding of data science, data engineering, application development, software engineering, and DevOps/MLOps.

Responsibilities

• Collaborate with data scientists and data engineers to design and implement scalable and efficient machine learning pipelines.
• Evaluate and optimize machine learning models for performance and scalability.
• Deploy machine learning models into production and monitor their performance.
• Manage data science infrastructure to streamline model development and deployment.
• Support developing and deploying high-quality Generative AI technologies, such as prompt engineering, RAG applications, and fine-tuning LLM models, using Azure infrastructure and capabilities like Azure AI Studio.
• Propose appropriate tools (languages, libraries, frameworks) for implementing projects.
• Work closely with infrastructure architects to design scalable and efficient solutions.
• Collaborate with multi-functional teams to integrate machine learning models into existing systems and processes.
• Stay updated with the latest advancements in machine learning, MLOps, and LLMOps techniques, and contribute to continuously improving our machine learning capabilities.
• Develop strong partnerships with data science teams, data offices, and IT partners to foster collaboration.
• Mentor associates and peers on MLOps standard methodologies.

Requirements

• Advanced degree in Data Science, Computer Science, Engineering, Physics, Statistics, or Math.
• Minimum of 5 years of combined experience in roles such as ML engineer, DevOps engineer, system architect, or data scientist in Big Data ecosystems or distributed/public cloud platforms.
• At least 3 years of hands-on experience with the Azure tech stack, including Databricks, MLFlow, Azure AI Studio, Langchain, and vector stores. Experience with Azure OpenAI and open-source models like Llama.
• Experience in operationalizing code through DevOps pipelines, including git, Jenkins, and code scanning.
• Proficiency in building and deploying machine learning models as REST-based APIs using frameworks like Flask.
• Strong programming skills in Python.
• Advanced SQL knowledge and experience with relational databases.
• Expertise in cloud infrastructure and technologies, including Cloud Computing, Linux OS, Docker, Kubernetes, and databases (RDBMS and NoSQL).
• Experience in building ETL pipelines for feature engineering on large-scale datasets using Spark.
• Familiarity with Large Language Models (LLMs) such as GPT-3 or BERT.
• Ability to deliver high-quality and pragmatic solutions under urgency.
• Familiarity with agile product delivery in an enterprise environment.
• Solid understanding of machine learning algorithms, statistical modeling, and data analysis techniques.
• Experience working in the insurance industry, ideally related to underwriting and product development.

Benefits

• A competitive salary and benefits packages.
• A growth trajectory that extends upward and outward, encouraging you to follow your passions and learn new skills.
• A focus on growing your career path with us.
• Flexible work policies and strong work-life balance.
• Professional development and leadership opportunities.