Senior Machine Learning Engineer - Hybrid

manulife; Boston (MA); Hybrid; USD 158,000 - 193,000; Full time

Job summary

Manulife seeks a Senior Machine Learning Engineer to design and implement production-grade ML platforms using MLOps/LLMOps best practices. You will work with data scientists and engineers to build scalable pipelines, evaluate models, and deploy them with robust monitoring.

The role also emphasizes mentoring peers in MLOps and staying current with Generative AI advances. You will collaborate across teams, craft scalable solutions with Docker/Kubernetes on cloud platforms, and contribute to

Qualifications

• Master's degree in Data Science, CS, CE, or related field with 3+ years ML experience.
• 3+ years deploying ML models via APIs, microservices, or cloud-based serving infra.
• 3+ years using Linux, Docker/Kubernetes, and cloud platforms (AWS/Azure/GCP).
• 3+ years building ETL pipelines and feature engineering for large data.
• 2+ years developing and deploying LLMs and transformer models.

Responsibilities

• Design, recommend, and implement platforms and infrastructure using MLOps/LLMOps best practices.
• Collaborate with data scientists and engineers to design scalable ML pipelines.
• Evaluate and optimize models for performance and scalability.
• Deploy models into production and monitor performance.
• Mentor associates on MLOps best practices.

Education

Master's degree in Data Science, Computer Science, Computer Engineering, or related field

Tools

Python

TensorFlow

PyTorch

Scikit-learn

Keras

XGBoost

OpenCV

Tesseract

Docker

Kubernetes

Linux

PostgreSQL

MySQL

Oracle

MongoDB

Cassandra

Elasticsearch

Redis

Apache Spark

PySpark

Spark SQL

Databricks

EMR

Azure AI Studio

Job description

Employer: John Hancock Life Insurance Company (USA)

Job Site : 200 Berkeley Street, Boston, MA 02116

Job Title: Senior Machine Learning Engineer

Job Duties:

• 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.

Work Arrangement requirement:

Hybrid from Boston office (3 days from office, 2 days from home)

Minimum Requirements:

• Master's degree or foreign equivalent in Data Science, Computer Science, Computer Engineering, or related field and 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.

Salary: $175,000 per year

The role being advertised is an existing vacancy.

About Manulife and John Hancock

Manulife Financial Corporation is a leading international financial services provider, helping people make their decisions easier and lives better. To learn more about us, visit https://www.manulife.com/en/about/our-story.html .

Manulife is an Equal Opportunity Employer

At Manulife/John Hancock, we embrace our diversity. We strive to attract, develop and retain a workforce that is as diverse as the customers we serve and to foster an inclusive work environment t