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Rockland Trust Company
AI

AI Engineer

Rockland Trust Company · MA, US · $401k

Actively hiring Posted about 1 month ago

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

Basic qualifications

  • 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

Preferred qualifications

  • 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
  • Languages: Python, SQL
  • ML Frameworks: TensorFlow, PyTorch, scikit-learn
  • GenAI Tools: LangChain, LangGraph, Hugging Face
  • MLOps: Docker, Kubernetes, MLflow, Weights & Biases
  • Cloud: AWS/Azure/GCP machine learning services
  • Data: Pandas, NumPy, vector databases Version Control: Git, CI/CD pipelines

Tags & focus areas

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Ai Ai Engineer Machine Learning Mlops Generative Ai
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