AI Engineer

Blue Cross Blue Shield of Massachusetts•Boston, MA

6d•$140,310 - $171,490

About The Position

The AI Engineer designs, develops, and deploys AI assistants and agentic applications using Google Cloud technologies to deliver business value. The role involves creating multi-step and multi-agent workflows, integrating enterprise tools and services, applying responsible AI practices, and building scalable production solutions. The engineer also develops evaluation and monitoring capabilities and works with product managers, data engineers, and business stakeholders to turn requirements into AI-powered solutions. The role calls for staying current with evolving AI models, tools, and techniques and recommending suitable adoption. The position is based in Boston or Hingham and is full time. The posted salary range is $140,310 to $171,490, and the role is also eligible for variable pay. The company describes a comprehensive benefits package, including paid time off, medical, dental, and vision insurance, a 401(k), and well-being benefits for eligible employees.

Requirements

Bachelor's or master's degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent practical experience.
At least 5 years of software engineering experience, including at least 2 years focused on AI/ML or LLM-based agentic development.
Hands-on experience with Google Cloud Platform services, particularly BigQuery, Cloud Storage, Cloud Run, or Cloud Functions.
Hands-on experience with Vertex AI Agent Builder, Vertex AI Search, or Dialogflow CX.
Experience with Google Cloud SDKs, including the Vertex AI SDK and Google Gen AI SDK, for model invocation, tuning, and deployment workflows.
Strong proficiency in Python and familiarity with ML/AI frameworks and libraries such as LangChain, LlamaIndex, TensorFlow, or PyTorch.
Experience building LLM-based agentic applications, including prompt engineering, function calling, embeddings, and retrieval-augmented generation.
Solid understanding of API design, microservices architecture, and cloud-native development patterns.
Familiarity with Git, CI/CD pipelines, and Infrastructure as Code such as Terraform or Cloud Build.
High school degree or equivalent required unless otherwise noted above.

Nice To Haves

Google Cloud certifications, such as Professional Machine Learning Engineer or Professional Cloud Architect.
Exposure to multi-agent architectures, tool-use patterns, and agentic AI frameworks.
Experience with vector databases such as Vertex AI Vector Search, AlloyDB, Pinecone, or Weaviate, and embedding-based retrieval.
Knowledge of MLOps practices, including experiment tracking, model versioning, A/B testing, and production monitoring.
Experience in regulated industries such as healthcare, finance, or insurance, with an understanding of data privacy and compliance requirements.

Responsibilities

Design and develop AI assistants using Vertex AI, including model selection, prompt engineering, grounding, retrieval-augmented generation (RAG), and agent orchestration.
Build multi-step AI agents with Google Agent Development Kit (ADK), including agent architectures with tool use, sub-agents, callbacks, and session management.
Develop and integrate applications using Google Cloud AI/ML SDKs for access to foundation models, embeddings, tuning, and prediction endpoints.
Design agentic architectures using sequential, loop, parallel, and custom agent hierarchies for multi-turn, multi-domain use cases.
Implement tool and function calling in ADK agents and connect agents to enterprise APIs, databases, and external services.
Apply responsible AI practices, including safety filters, bias detection, guardrails, hallucination mitigation, and content moderation.
Architect scalable, production-grade solutions using Vertex AI Agent Builder, Vertex AI Search, Cloud Run, Cloud Functions, Pub/Sub, BigQuery, and other GCP services.
Develop production evaluation and monitoring frameworks to measure assistant quality, latency, cost, and user satisfaction, including use of ADK evaluation and tracing capabilities.
Collaborate with product managers, data engineers, and business stakeholders to translate requirements into AI-powered solutions.
Stay current with AI models, tools, and techniques, evaluate emerging options, and recommend adoption where appropriate.