Senior Machine Learning Engineer - Hybrid

Manulife•Boston, MA

8d•$107,450 - $199,550•Hybrid

About The Position

The Advanced Analytics Team at John Hancock Life Insurance Company (USA) is seeking a Senior Machine Learning Engineer to join their U.S. based team. This role involves packaging models, automating workflows, and operationalizing analytics solutions to create business value for the organization's insurance division. The engineer will design, recommend, and implement platforms and infrastructure using MLOps/LLMOps best practices, collaborate with data scientists and engineers on scalable machine learning pipelines, and optimize models for performance and scalability. Responsibilities also include deploying and monitoring models in production, managing data science infrastructure, supporting Generative AI technologies (prompt engineering, RAG, fine-tuning LLMs on Azure AI Studio), proposing appropriate tools, and working with infrastructure architects on scalable solutions. The role requires collaboration with cross-functional teams for integration, staying updated on ML advancements, and mentoring associates on MLOps best practices.

Requirements

Master’s degree or foreign equivalent in Data Science, Computer Science, Computer Engineering, or related field
3 years of machine learning experience
3 years of experience developing and deploying machine learning models for model training, optimization, evaluation, and production deployment through APIs, microservices, or cloud-based serving infrastructure using Python, TensorFlow, PyTorch, Scikit-learn, Keras, or XGBoost
3 years of experience deploying and managing infrastructure using Linux OS, containerization technologies (Docker, Kubernetes), relational databases (PostgreSQL, MySQL, and Oracle) and NoSQL databases (MongoDB, Cassandra, Elasticsearch and Redis) using AWS, Azure or GCP cloud platforms
3 years of experience designing and building scalable ETL pipelines and feature engineering workflows for large-scale datasets using distributed processing frameworks including Apache Spark (PySpark, Spark SQL), Hadoop ecosystem tools, or cloud-based big data services including Databricks and EMR
2 years of experience developing and deploying Large Language Models including BERT, GPT-series, T5, or LLaMA, or other transformer-based NLP models using cloud-based platforms and open-source frameworks
3 years of experience designing hybrid machine learning systems combining rule-based decision engines with ML models for fraud detection, compliance, claims adjudication, or automated decision-making in regulated environments
3 years of experience applying machine learning algorithms, statistical modeling, and data analysis techniques for model optimization, generating actionable insights, and working with structured and unstructured data to solve business problems in financial services, fintech, insurance, or other regulated industries
3 years of experience with Agile development methodologies including Scrum, Kanban, or SAFe for sprint planning, iterative development cycles, and cross-functional team collaboration in enterprise environments
2 years of experience developing and deploying computer vision models for document processing, OCR, information extraction, or image classification using OpenCV, Tesseract, or cloud-based vision APIs

Responsibilities

Design, recommend, and implement platforms and infrastructure using MLOps/LLMOps best practices
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 performance
Manage data science infrastructure to streamline model development and deployment
Support development and deployment of high-quality Generative AI technologies including prompt engineering and RAG applications, and fine-tuning LLM models using Azure AI Studio
Propose appropriate tools including languages, libraries, and frameworks for implementing projects
Work closely with infrastructure architects to design scalable and efficient solutions
Collaborate with cross-functional teams to integrate machine learning models into existing systems and processes
Keep abreast of latest advancements in machine learning, MLOps, and LLMOps techniques, and contribute to continuously improving organization’s machine learning capabilities
Mentor associates and peers on MLOps best practices