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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Blue Cross and Blue shield of Massachusetts, Inc. - **Location:** Hingham, MA, United States - **Experience:** Expert - **Salary:** $140,310.0 - $171,490.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, BigQuery, Cloud Computing, Cloud Storage, Encodings, Databases, Python (Programming Language), Machine Learning, Language Modeling, Performance Tuning, Tensorflow, Session Management, Software Engineering, Pinecone, Google Cloud, Pytorch, LangChain, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Prompt Engineering, Model Validation, Llamaindex, Generative AI, Build Server, Agentic-AI, Vertex AI Vector Search, Git, AI Platforms, Information Technology, Google Cloud Functions, Weaviate, Virtual Agents, Invoking Functions, Terraform, Software Version Control, Microservices - **Published:** November 4, 2026 - **Apply:** https://jobs.localjobnetwork.com/apply/add/88770402/1 ## About the Role * Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field (or equivalent practical experience). * 5+ years of experience in software engineering, with at least 2+ years focused on AI/ML or LLM-based agentic development. * Hands-on experience withGoogle Cloud Platformservices, particularly BigQuery, Cloud Storage, Cloud Run, or Cloud Functions. * Hands-on experience withVertex AI Agent Builder, Vertex AI Search, or Dialogflow CX. * Hands-on experience Experience with Google Cloud SDKs (Vertex AI SDK, Google Gen AI SDK) for model invocation, tuning, and deployment workflows. * Strong proficiency inPythonand familiarity with ML/AI frameworks and libraries (e.g., LangChain, LlamaIndex, TensorFlow, PyTorch). * Experience building agentic applications that leveragelarge language models (LLMs)- including prompt engineering, function calling, embeddings, and retrieval-augmented generation. * Solid understanding ofAPI design, microservices architecture, and cloud-native development patterns. * Familiarity withversion control (Git), CI/CD pipelines, and Infrastructure as Code (e.g., Terraform, Cloud Build). Preferred Qualifications * Google Cloud certifications (e.g., Professional Machine Learning Engineer, Professional Cloud Architect). * Exposure tomulti-agent architectures, tool-use patterns, and agentic AI frameworks. * Experience withvector databases(e.g., Vertex AI Vector Search, AlloyDB, Pinecone, Weaviate) and embedding-based retrieval. * Knowledge ofMLOpspractices including experiment tracking, model versioning, A/B testing, and monitoring in production. * Experience working in regulated industries (healthcare, finance, insurance) with an understanding of data privacy and compliance requirements. Minimum Education Requirements: High school degree or equivalent required unless otherwise noted above ## Description We are looking for an AI Engineer to join our AI Engineering team focused on designing, building, and deploying intelligent AI assistants powered by Google Cloud Platform (GCP) and Vertex AI. You will work at the intersection of large language models, cloud infrastructure, and product engineering to deliver conversational AI experiences that drive real business value. What You'll Do * Design and develop AI assistantsusing Vertex AI, including model selection, prompt engineering, grounding, retrieval-augmented generation (RAG), and agent orchestration. * Build intelligent, multi-step AI agentsusing theGoogle Agent Development Kit (ADK), designing agent architectures with tool use, sub-agents, callbacks, and session management for complex conversational workflows. * Develop and integrate applicationsusing theGoogle Cloud AI/ML SDKs(Vertex AI SDK for Python, Google Gen AI SDK) for programmatic access to foundation models, embeddings, tuning, and prediction endpoints. * Design agentic architecturesleveraging ADK's orchestration capabilities including sequential agents, loop agents, parallel agents, and custom agent hierarchies to handle multi-turn, multi-domain use cases. * Implement tool and function calling patternswithin ADK agents, connecting them to enterprise APIs, databases, and external services to enable grounded, domain-specific responses. * Implement responsible AI practicesincluding safety filters, bias detection, guardrails, hallucination mitigation, and content moderation. * Architect scalable, production-grade solutionsusing Vertex AI Agent Builder, Vertex AI Search, Cloud Run, Cloud Functions, Pub/Sub, BigQuery, and other GCP services. * Develop evaluation and monitoring frameworksto measure assistant quality, latency, cost, and user satisfaction in production - including ADK's built-in evaluation and tracing capabilities. * Collaborate with product managers, data engineers, and business stakeholdersto translate requirements into AI-powered solutions. * Stay current with the rapidly evolving AI landscape, evaluate emerging models, tools, and techniques, and recommend adoption where appropriate.