Top AI Engineer Jobs in Boston, MA

Build and deploy generative AI solutions, including APIs, MCP servers, AI agents, LLM integrations, and full-stack applications. Develop scalable AWS cloud services, observability systems, evaluation pipelines, and production agent workflows. Collaborate with Product, UX, Security, Legal, and engineering teams to design interfaces, adapt AI tools to company codebases, and support customer experience and developer productivity use cases.

Build and deploy foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for latency, cost, throughput, scalability, and hardware utilization. Own complex architectures, reliability SLOs, security and compliance reviews, technical roadmaps, and cross-functional delivery while mentoring engineering teams.

Leads the design and implementation of AI-powered Finance workflows, agents, copilots, and integrations across ERP, billing, procurement, and related systems. Establishes AI architecture, governance, security, testing, observability, and responsible-use practices. Partners with Finance, IT, Security, Legal, and Audit to identify automation opportunities, define success metrics, build reusable frameworks, and enable nontechnical teams. Requires technical leadership, enterprise financial systems integration, LLM experience, and the ability to build scalable 0-to-1 solutions.

Develop, train, evaluate, optimize, and debug machine learning models for consumer audio applications. Focus on efficient AI and Edge ML by reducing latency, memory usage, and computational demands. Collaborate with software, embedded systems, and hardware engineers to deploy models on resource-constrained devices. Explore techniques such as quantization, pruning, and model compression while contributing to emerging AI technologies and practical product development.

Develops, tests, evaluates, and deploys machine learning models using established architectures and frameworks. Supports feature engineering, data preprocessing, automated training and monitoring pipelines, and production AI integration. Collaborates with engineering and product teams on AI requirements, researches methods to improve model performance, and supports urgent IT incidents. The role requires strong Python and machine learning experience, knowledge of deep learning and generative AI, and advanced quantitative education.

Develop, test, deploy, and serve machine learning models and agentic AI capabilities. Build feature engineering, experimentation, training, monitoring, and REST-based model-serving workflows. Collaborate with data science, engineering, product, and MLOps teams to productionize AI solutions at enterprise scale using distributed computing, Databricks, cloud platforms, and containerized infrastructure. Support model performance improvements, generative AI initiatives, and urgent IT escalations.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, ensure security and compliance, shape long-term AI roadmaps, and mentor engineering staff.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, throughput, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor engineering colleagues.

Design, develop, optimize, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, orchestration, governance, and observability. Lead cost-performance optimization across production AI platforms, establish engineering standards, contribute to technical roadmaps, lead design reviews, and mentor senior AI engineers. Collaborate with research, engineering, program management, and product teams to deliver scalable, responsible AI products.

Design and implement AI agents, plugins, automations, and enterprise integrations for HR Technology platforms. Lead requirements gathering, technical analysis, solution design, testing, documentation, deployment, security, and data integrity efforts across HR, IT, and business stakeholders. Integrate platforms including ServiceNow, Workday, and other enterprise applications while supporting the HR automation and AI roadmap.

Build developer tooling, reusable frameworks, AI agent workflows, MCP integrations, and cloud-native services that improve engineering productivity. Evaluate and roll out AI development technologies, establish standards and guardrails, support authentication platform evolution, and improve AWS service and job creation paths. The role also involves documentation, collaboration with engineering and product teams, and improving system reliability and performance.

Leads enterprise AI platform adoption by embedding with IT teams, building agentic workflows and integrations, improving software delivery and operations, and measuring engineering efficiency. Applies AI across the SDLC, DevSecOps, observability, security, governance, and legacy modernization. Translates field learnings into reusable platform capabilities, influences senior stakeholders, mentors engineers, and serves as a technical escalation point without direct management responsibility.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI engineering standards, lead governance and design reviews, contribute to technical roadmaps, and mentor senior AI engineering leaders.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI performance governance, lead design reviews, define technical roadmaps, and mentor senior AI engineering talent.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to strategic roadmaps, and mentor senior technical staff.

Leads complex AI and cloud-native incubation projects, designing and implementing scalable services across public cloud, datacenter, and edge environments. Develops Generative AI, microservices, and distributed-system solutions; evaluates emerging technologies through prototypes and research spikes; guides architecture, security, reliability, testing, CI/CD, and observability. Leads small multidisciplinary engineering teams, collaborates with stakeholders, mentors engineers, and advances innovative capabilities across multiple parallel projects.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, orchestration, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Establish AI governance standards, lead technical reviews, shape long-term platform strategy, and mentor senior AI engineers. Collaborate with engineering, research, product, and program teams to deliver responsible AI products.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, orchestration, cost-performance governance, technical reviews, roadmap development, and responsible AI standards. Mentor senior technical staff and apply advanced optimization techniques to improve model scalability, latency, throughput, hardware utilization, and inference cost.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI engineering standards, lead technical reviews, and mentor senior engineering leaders while applying emerging research responsibly in production.

Build and operate Affirm’s AI platform, including LLM and MCP gateways, agent runtimes, evaluation services, and governance controls. Design highly available backend systems, manage identity, quotas, routing, caching, reliability, observability, and security for AI workloads. Own projects from design through launch and on-call, establish engineering standards, collaborate across teams, mentor engineers, and help scale AI systems into production.

The AI Application Engineer will design and deploy AI solutions, integrate them into cloud services, and collaborate with teams to enhance operational workflows.

Build and deploy scalable generative AI platform services, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, reliability, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor senior technical staff.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead orchestration, cost-performance governance, technical standards, design reviews, and roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost efficiency while mentoring senior technical staff and ensuring responsible AI practices.

Lead the design, development, and adoption of production-grade AI systems within a strategic business domain. Define AI roadmaps, identify high-impact opportunities, make architecture decisions, establish evaluation and observability practices, measure business impact, mentor engineers, and drive AI transformation across cross-functional teams. The role focuses on LLMs, RAG, AI agents, scalable infrastructure, and reliable enterprise AI products.

Designs and deploys AI-powered features and scalable AI/ML services across SaaS products. Responsibilities include translating business problems into AI solutions, integrating large language models, preparing data, evaluating model performance, writing Python, building secure deployment infrastructure, and collaborating with product, data, and cloud operations teams. The role requires production AI/ML experience, scalable data infrastructure expertise, AWS familiarity, and modern DevOps practices.