AI Engineer

Rockland Trust•MA, MA

4h•$110,000 - $130,000

About The Position

We are seeking an experienced AI Engineer to join our AI team and drive the development and deployment of advanced machine learning and generative AI solutions. This role will be instrumental in building intelligent systems that leverage both traditional ML techniques and cutting-edge generative AI technologies to deliver business value.

Requirements

5+ years of experience in data science, machine learning, or related field
Strong expertise in generative AI technologies including LLMs, prompt engineering, and context management
Hands-on experience building RAG systems with vector databases and semantic search
Proficiency with LangChain, LangGraph, and agent-based architectures
Experience with Model Context Protocol (MCP) and A2A context engineering patterns
Deep understanding of traditional machine learning algorithms (regression, classification, clustering, time series)
Strong MLOps experience including CI/CD pipelines, model versioning, and monitoring frameworks
Proficiency with Hugging Face ecosystem (Transformers, Datasets, Hub)
Experience with TensorFlow and/or PyTorch for model development
Strong Python programming skills with experience in production-grade code
Experience designing and implementing feedback loops for continuous model improvement
Knowledge of cloud platforms (AWS, Azure, or GCP) for ML deployment
Excellent communication skills with ability to explain complex technical concepts to non-technical stakeholders

Nice To Haves

Experience in regulated industries (financial services, healthcare)
Knowledge of data governance and model risk management frameworks
Experience with distributed training and large-scale model deployment
Familiarity with other frameworks like Anthropic's Claude API, OpenAI API
Experience with vector databases (Pinecone, Weaviate, Chroma)
Understanding of prompt engineering and fine-tuning techniques
Contributions to open-source ML/AI projects

Responsibilities

Design, develop, and deploy production-grade machine learning models and generative AI applications using state-of-the-art frameworks and methodologies
Build and optimize Retrieval Augmented Generation (RAG) pipelines for enterprise knowledge systems and intelligent document processing
Implement Model Context Protocol (MCP) and agent-to-agent (A2A) context engineering solutions for complex AI orchestration
Develop feedback loops and monitoring systems to continuously improve model performance and ensure reliability
Architect and maintain MLOps pipelines for model training, versioning, deployment, and monitoring
Create AI agents using LangChain and LangGraph frameworks for autonomous decision-making and workflow automation
Collaborate with data engineering teams to build robust data pipelines and feature engineering workflows
Mentor junior data scientists and contribute to the development of AI best practices and standards