Senior Machine Learning Engineer - Hybrid

Employer:

John Hancock Life Insurance Company (USA) Job Site: 200 Berkeley Street, Boston, MA 02116

Job Title:

Senior Machine Learning Engineer Job Duties

Package models, automate workflows, and operationalize analytics solutions to generate business value for organization’s insurance division as member of U.S. based Advanced Analytics Team.

Duties include:

• 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; and
• Mentor associates and peers on MLOps best practices.

Work Arrangement requirement:
Hybrid from Boston office (3 days from office, 2 days from home)

Master’s degree or foreign equivalent in Data Science, Computer Science, Computer Engineering, or related field and 3 years of machine learning experience.

Experience must include the following, which may be gained concurrently:

Master’s degree or foreign equivalent in Data Science, Computer Science, Computer Engineering, or related field and 3 years of machine learning experience.

Experience must include the following, which may be gained concurrently:

Master’s degree or foreign equivalent in Data Science, Computer Science, Computer Engineering, or related field and 3 years of machine learning experience.

Experience must include the following, which may be gained concurrently:

• 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, Tensor Flow, 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 No
  
  SQL 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…