AI Engineer

Rockland Trust

Massachusetts

On-site

USD 140,000 - 200,000

Full time

14 days+

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Benefits offered by this job

Health Insurance

Dental Insurance

401K plan

DC Plan

Long term Disability insurance

Life Insurance

Tuition Assistance

Wellness program

Job summary

Rockland Trust is seeking an experienced AI Engineer to develop and deploy advanced ML and generative AI solutions in Massachusetts onsite, delivering business value through intelligent systems.

The role focuses on production-grade ML models, RAG implementations, and scalable AI workflows using TensorFlow, PyTorch, LangChain, and cloud platforms. You will mentor junior data scientists and help establish AI best practices across the team.

Qualifications

Five+ years of experience in data science, ML, or related field.
Proven expertise in generative AI technologies including LLMs, prompt design, and context management.
Experience building RAG systems using vector databases and semantic search.
Familiarity with LangChain, LangGraph and agent-based architectures.
Strong MLOps background including CI/CD, model versioning, and monitoring tools.
Hands-on experience with TensorFlow and/or PyTorch for model development.

Responsibilities

Create and implement production ready ML models and generative AI applications using modern frameworks and approaches
Develop and optimize Retrieval Augmented Generation pipelines for enterprise knowledge bases and automated document workflows
Integrate Model Context Protocol (MCP) and agent to agent context engineering solutions to orchestrate complex AI workflows
Establish feedback loops and monitoring to drive continuous performance improvements and reliability
Design and sustain MLOps pipelines covering training, versioning, deployment, and monitoring
Build AI agents with LangChain and LangGraph to enable autonomous decision making and workflow automation
Collaborate with data engineering teams to construct robust data pipelines and feature engineering workflows
Mentor junior data scientists and help establish AI best practices and standards

Skills

Data science

Machine learning

Generative AI

LangChain

LangGraph

Context engineering

Python

TensorFlow

PyTorch

MLOps

CI/CD

Cloud platforms

Pandas

NumPy

Hugging Face

AWS

Azure

GCP

Tools

Python

SQL

TensorFlow

PyTorch

scikit-learn

LangChain

LangGraph

Hugging Face

Docker

Kubernetes

MLflow

Weights & Biases

AWS

Azure

GCP

Pinecone

Weaviate

Chroma

OpenAI API

Anthropic Claude API

Transformers

Datasets

Hugging Face Hub

Git

Job description

Rockland Trust is seeking an experienced AI Engineer to develop and deploy advanced machine learning and generative AI solutions in Massachusetts onsite, delivering business value through intelligent systems.

Responsibilities

Create and implement production ready ML models and generative AI applications using modern frameworks and approaches
Develop and optimize Retrieval Augmented Generation pipelines for enterprise knowledge bases and automated document workflows
Integrate Model Context Protocol (MCP) and agent to agent context engineering solutions to orchestrate complex AI workflows
Establish feedback loops and monitoring to drive continuous performance improvements and reliability
Design and sustain MLOps pipelines covering training, versioning, deployment, and monitoring
Build AI agents with LangChain and LangGraph to enable autonomous decision making and workflow automation
Collaborate with data engineering teams to construct robust data pipelines and feature engineering workflows
Mentor junior data scientists and help establish AI best practices and standards

Requirements

Minimum five years of experience in data science, ML, or related fields
Proven expertise in generative AI technologies including large language models, prompt design, and context management
Practical experience building RAG systems using vector databases and semantic search
Experience with LangChain, LangGraph and agent based architectures
Familiarity with MCP and A2A context engineering patterns
Strong grasp of traditional ML algorithms such as regression, classification, clustering, and time series
Solid MLOps background including CI/CD, model versioning, and monitoring tools
Experience with the Hugging Face ecosystem (Transformers, Datasets, Hub)
Hands on experience with TensorFlow and/or PyTorch for model development
Advanced Python programming skills with production grade code experience
Experience designing and implementing feedback loops for ongoing model improvement
Experience deploying ML solutions on cloud platforms such as AWS, Azure, or GCP
Excellent communication skills able to convey complex technical concepts to non technical stakeholders

Technologies