AI Engineer

• Design, build, and deploy production-ready AI and ML solutions supporting the Long-Term Care program across John Hancock and Manulife
• Partner with Data Scientists, Data Engineers, Business Analysts, and Product teams to translate business needs into scalable AI products
• Build and maintain reusable ML and GenAI pipelines covering data processing, feature engineering, model training, evaluation, deployment, and monitoring
• Operationalize traditional ML models and predictive analytics solutions, including classification, regression, forecasting, risk scoring, segmentation, and anomaly detection
• Implement GenAI and LLM-based solutions, including RAG, prompt orchestration, document intelligence, summarization, classification, and workflow automation
• Deploy AI services using containerization, CI/CD, automated testing, version control, and cloud-native infrastructure
• Monitor models for performance, drift, bias, accuracy degradation, latency, cost, and reliability using MLOps and LLMOps practices
• Build observability capabilities with logging, tracing, metrics, alerts, dashboards, and service-level monitoring
• Collaborate with Risk, Legal, Compliance, Security, Architecture, and Cloud teams to ensure secure, compliant, explainable solutions
• Support model governance, documentation, validation, auditability, model lineage, and responsible AI controls
• Evaluate and adopt tools and platforms across Azure, Databricks, Azure OpenAI, MLflow, vector databases, and internal AI platforms
• Integrate AI services with business workflows through APIs, event-driven architecture, batch pipelines, and enterprise applications
• Improve solution quality, scalability, maintainability, and cost efficiency
• Stay current with AI, ML, GenAI, LLMOps, software engineering, cloud platforms, and financial services technology trends
• Mentor junior engineers and data scientists on production engineering, clean code, testing, monitoring, and MLOps/LLMOps best practices

• Architect and build new internal AI systems end to end, from initial design through production
• Own retrieval quality, permissions, security, and reliability for trustworthy AI at company scale
• Work hands-on across LLMs, retrieval, agents, and integrations
• Embed with teams across the company and function leaders to ship AI into real workflows
• Collaborate weekly with executive leadership
• Leave behind systems and owners that outlast embedded engagements
• Drive build-versus-buy evaluations and rapid proofs of concept
• Make technical decisions that inform how Jobber operates and makes decisions

Principal Engineer, AI

• Design and implement core capabilities for an enterprise-grade Agent platform, including orchestration patterns such as ReAct, Plan-and-Execute, and Supervisor
• Implement tool execution, context and memory management, and safety guardrails
• Design Agent execution and governance mechanisms, including Human-in-the-Loop approval workflows, multi-tenant permission isolation, policy enforcement, and secure execution controls
• Build reusable Agent Skills, standardized tool interfaces, and a scalable tool ecosystem integrated with NetBrain platform capabilities and business workflows
• Design LLM post-training strategies, including domain-specific SFT, DPO/RLHF-based preference alignment, and LoRA fine-tuning
• Build self-learning feedback loops using production traces, user feedback, and evaluation results
• Analyze and optimize LLM behavior, including instruction following, tool calling, structured output generation, contextual understanding, reasoning stability, and hallucination mitigation
• Build LLM and Agent evaluation frameworks and automated regression pipelines
• Establish release quality gates and hallucination-detection mechanisms
• Build AI observability with distributed tracing, structured logging, metrics, dashboards, and alerting
• Diagnose and resolve production AI failures and unexpected model behavior changes
• Design reliable backend services with asynchronous and concurrent processing, retries, timeouts, caching, rate limiting, and fault isolation
• Optimize latency, throughput, token consumption, and infrastructure cost for platform SLA requirements
• Lead technical design for critical modules and system-level capabilities
• Collaborate with Engineering, Product, QA, and other teams to deliver production solutions
• Prototype, benchmark, and productionize emerging technologies such as GraphRAG, Knowledge Graphs, MCP, LLM post-training, and Agent self-learning
• Evaluate Agent frameworks and infrastructure and provide recommendations for platform architecture and product technology strategy

• Develop, maintain, and enhance CASI, NFPA’s AI system, related AI systems, and applications that leverage them
• Design, develop, test, maintain, and improve AI systems and web-based business applications
• Develop and support generative AI solutions, including RAG systems, agentic workflows, tool calling, agents and skills, prompt and context engineering, MCP servers, vector databases, semantic search, and large-scale document ingestion and retrieval
• Integrate software systems, application components, and third-party programs
• Collaborate with software developers, business analysts, architects, vendors, partners, and teams across the organization throughout the software development lifecycle
• Apply application architecture knowledge to technical decision-making and problem-solving
• Design and implement observable, high-throughput RESTful APIs and integrations
• Produce clean, efficient, well-designed, and testable code
• Design, develop, and unit test applications according to established development standards
• Participate in code reviews and mentor and assist other developers with software development best practices
• Analyze, troubleshoot, and resolve technical and application issues
• Develop and maintain technical documentation
• Work effectively within legacy application architectures when required
• Collaborate across teams to meet changing priorities and deadlines while proactively identifying issues and developing long-term solutions

Senior Software Engineer – AI

• Embed with Aerospace & Defense customers on their sites and inside their systems
• Learn how customer operations work and design and ship software collaboratively
• Design, build and test full-stack features across services, APIs, data, persistence, integrations and web interfaces
• Connect IFS to ERP, CRM, IoT, telemetry, legacy and mission systems
• Own solution architecture in real customer contexts
• Manage automated testing, CI/CD, deployment, observability and operational reliability across cloud and customer environments
• Use AI-assisted and agentic development tooling and take AI-powered capabilities into customer environments
• Feed field learnings back into the core product and roadmap
• Collaborate with product management and wider engineering teams
• Serve as a trusted technical voice for customers, partners and operations leaders

AI Engineer jobs in Massachusetts

AI Engineer

Principal Engineer, AI

AI Engineer

Senior Software Engineer – AI

Forward Deployed AI Engineer – Aerospace & Defense