Category/Area of Expertise: Data-Science & Analytics
Job Requisition: 558916
Address: USA-MA-Quincy-1385 Hancock Street
Store Code: Data Science (5157585)
Primary Purpose:AI/ML Engineer III builds machine learning models, implements training and evaluation workflows, and supports integration of AI capabilities into production environments.
In close collaboration with our Data Scientists, Data Engineers, and MLOps Engineers this role will scale machine
learning algorithms, agentic solutions, and develop novel approaches to enterprise ai. This position plays a pivotal role in a fast-growing, globally focused network of data scientists, machine learning engineers, and AI practitioners, working to improve enterprise AI efficiency gains and agentic workflows.
*Our flexible/hybrid work schedule includes 3 in-person days at one of our core locations and 2 remote days. Our core office location for this role is Quincy, MA. *Applicants must be currently authorized to work in the United States on a full-time basis.Duties and Responsibilities:- Explore, design, and implement Generative AI and Retrieval-Augmented Generation (RAG) approaches to support enterprise AI use cases.
- Design and develop agentic AI solutions, leveraging appropriate agent architectures, system design patterns, tools, and frameworks.
- Develop RAG pipelines, including document chunking, embeddings, indexing, retrieval strategies, and integration with large language models (LLMs).
- Design and develop AI-powered chatbots and conversational applications using LLMs, enterprise data, and retrieval-based techniques.
- Evaluate and prototype emerging Generative AI, agentic AI, and retrieval techniques to improve solution quality, scalability, and maintainability.
- Build model serving applications for serving using REST microservices. Identify constraints, strengths, and weaknesses of technology options for model serving
- Assist in developing automated ML training and monitoring pipelines.
- Collaborate with engineering and product partners to refine AI solution requirements.
- Works closely within a team environment to incorporate collaborative business plans utilizing both internal and external data partners. Keep up with latest trends in industry on machine learning and gen ai space to enhance model performance
- May be called upon to support critical escalations and must be available during urgent IT incidents as needed.
Qualifications:- Master's degree or PhD in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field required; equivalent advanced experience in applied data science may be considered.
- 5+ years of AI/ML engineering experience.
- Expert coding ability in Python
- Strong understanding of agentic AI system design patterns and hands-on experience developing AI agents and agent-based workflows.
- Strong software engineering fundamentals, including data structures, algorithms, object-oriented design, modular development, testing, and maintainable code.
- Strong experience with scikit-learn, PyTorch, TensorFlow, and Hugging Face libraries and frameworks.
- Hands-on experience building Retrieval-Augmented Generation (RAG) solutions, including embeddings, document chunking, indexing, retrieval, and integration with LLMs.
- Experience developing LLM-powered chatbots and conversational AI applications.
- Understanding of Generative AI and LLM application architecture, including retrieval, context management, and agentic workflows.
- Knowledge of API development best practices, model management, code versioning and agile methodologies
- Understanding of deep learning and generative AI fundamentals.
- Strong analytical and communication skills.
Preferred Qualifications:- Experience in deploying models on azure cloud.
- Experience with cloud ML platforms.
- Experience deploying models on very large data using Databricks, Kafka and Kubernetes
- Experience working on cloud infrastructure ( Azure Preferred)
- Experience training and serving models on GPU(s)
Salary Range: $125,040 - $187,560
Actual compensation offered to a candidate may vary based on their unique qualifications and experience, internal equity, and market conditions. Final compensation decisions will be made in accordance with company policies and applicable laws.
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