1 hour ago
Base Salary
$107k - $200k/yr
Responsibilities
- Design, recommend, and implement MLOps and LLMOps platforms and infrastructure.
- Collaborate with data scientists and data engineers to build scalable machine learning pipelines.
- Evaluate, optimize, deploy, and monitor machine learning models in production.
- Manage data science infrastructure and integrate machine learning models into existing systems.
- Support generative AI development and deployment, including prompt engineering, RAG applications, and LLM fine-tuning.
- Design scalable ETL pipelines and feature-engineering workflows for large-scale datasets.
- Develop computer vision models for document processing, OCR, information extraction, and image classification.
- Design hybrid rule-based and machine learning systems for fraud detection, compliance, claims adjudication, and automated decision-making.
- Propose appropriate languages, libraries, frameworks, and tools for project implementation.
- Collaborate with infrastructure architects and cross-functional teams to develop efficient solutions.
- Mentor associates and peers on MLOps best practices and contribute to improvements in machine learning capabilities.
Requirements
- Master’s degree or foreign equivalent in Data Science, Computer Science, Computer Engineering, or a related field.
- At least 3 years of machine learning experience.
- At least 3 years of experience developing and deploying machine learning models for training, optimization, evaluation, and production serving using Python and relevant machine learning frameworks.
- At least 3 years of experience managing infrastructure with Linux, Docker, Kubernetes, relational and NoSQL databases, and AWS, Azure, or GCP.
- At least 3 years of experience building scalable ETL pipelines and feature-engineering workflows with distributed processing frameworks or cloud-based big data services.
- At least 2 years of experience developing and deploying large language models, including transformer-based NLP models, using cloud platforms and open-source frameworks.
- At least 3 years of experience designing hybrid machine learning systems for regulated-industry use cases.
- At least 3 years of experience applying machine learning algorithms, statistical modeling, and data analysis in financial services, fintech, insurance, or another regulated industry.
- At least 3 years of experience with Agile development methodologies including Scrum, Kanban, or SAFe.
- At least 2 years of experience developing and deploying computer vision models using OpenCV, Tesseract, or cloud-based vision APIs.
Benefits
- Hybrid work arrangement in Boston with 3 days in the office and 2 days from home.
- Eligible employees may receive health, dental, mental health, vision, disability, life, AD&D, adoption/surrogacy, wellness, and employee/family assistance benefits.
- Retirement savings plans, pension/401(k) plans, global share ownership with employer matching, and financial education resources are available to eligible employees.
- U.S. paid time off includes up to 11 paid holidays, 3 personal days, 150 hours of vacation, and 40 hours of sick time, plus statutory leaves of absence.
Tech Stack
Apache CassandraApache HadoopApache SparkAWSAzureDatabricksDockerElasticsearchGoogle Cloud PlatformKerasKubernetesLinuxMongoDBMySQLOpenCVPostgreSQLPythonPyTorchRedisscikit-learnTensorFlowXGBoost
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