Top AI Engineer Jobs in Boston, MA

Designs and develops Azure-based generative AI solutions, including conversational applications, retrieval-augmented generation pipelines, agent workflows, cloud-native microservices, and LLM evaluation systems. The role uses Python, Azure AI Services, Azure OpenAI, LangGraph, vector databases, and DevOps practices to deploy and maintain client solutions. Responsibilities also include managing workstreams, communicating technical tradeoffs, reviewing deliverables, mentoring junior colleagues, and collaborating with clients.

Leads client-facing design and delivery of agentic and generative AI solutions on Azure. Oversees architecture, agent orchestration, RAG pipelines, LLMOps, deployment, testing, security, monitoring, and maintenance. Manages workstreams, budgets, timelines, and deliverables while coordinating with clients and engineering teams. Coaches engineers, reviews technical work, evaluates model behavior, and guides AI-assisted development using Python, C#, and modern AI tools.

Leads client-facing applied AI engineering engagements in healthcare, from solution design through deployment. Translates clinical and health services needs into AI requirements, architectures, and roadmaps; designs RAG, memory, prompt, and agent orchestration components; guides data, model integration, and cloud implementation; evaluates model performance; advises on AI platforms and governance; and develops LLMOps and AgentOps practices. Coaches teams, resolves delivery challenges, and communicates technical decisions to clients and leaders.

Lead architecture and delivery of AI-native software for Flow’s conversational service desk. Build agent runtimes, tool integrations, prompt and context layers, retrieval systems, and evaluation harnesses. Establish AI-assisted development practices, mentor engineers, set standards for quality and observability, and partner across product and engineering. Own production quality, defect triage, release readiness, and governance for reliable, secure, enterprise-scale AI capabilities.

Leads the technical development and delivery of AI/ML-enabled RF, communications, and networking capabilities for national-security missions. Responsibilities include translating customer needs into architectures and plans, leading engineers and researchers, managing technical risks and interfaces, designing RF and sensor-data systems, overseeing integration and testing, and transitioning solutions to operational environments. The role focuses on RF sensing, Open RAN, 5G/6G, edge AI, waveform adaptation, and high-throughput, low-latency software.

Leads enterprise AI initiatives by designing, building, deploying, and maintaining scalable AI-enabled solutions and GenAI tools. Establishes engineering standards, evaluates complex architectures, mentors colleagues, and collaborates with product, infrastructure, security, operations, and senior leadership. The role focuses on modern software engineering, cloud applications, AI-enabled SDLC automation, developer productivity, reliability, performance, security, and cost optimization.

Designs, develops, and deploys enterprise AI applications using LLMs, agentic frameworks, RAG, fine-tuning, and multi-agent systems. Leads scalable AI platform development, integrates models with enterprise products, establishes evaluation benchmarks, and ensures compliance with architecture, security, risk, governance, and responsible AI standards. Advises leadership, resolves complex technical challenges, develops reusable tools, and mentors engineers while collaborating across engineering, product, architecture, risk, and business teams.

Develop, integrate, test, debug, and maintain AI/ML software for RF sensing, waveform generation, communications, networking, and intelligence workflows. Implement defined features, APIs, data-processing capabilities, and integrations; support testing, demonstrations, releases, and operational environments. Collaborate with software, systems, RF, communications, and DevSecOps teams while improving system reliability, performance, maintainability, and testability. The role offers increasing ownership of technical components supporting mission-critical national-security applications.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, and observability. Lead multi-model orchestration, cost-performance governance, architecture reviews, technical roadmaps, and responsible AI standards. Mentor senior technical staff and collaborate with engineers, scientists, product managers, and program managers to deliver scalable AI products.

Design, develop, deploy, and support foundational AI systems, including large language model inference, agents, similarity search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, scalability, and hardware utilization. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor senior technical staff.

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

Build and deploy production-grade AI and machine learning systems supporting biomedical research and drug discovery. Own technical solutions from architecture through deployment, including data pipelines, retrieval systems, knowledge systems, inference services, evaluation frameworks, and human-in-the-loop workflows. Partner with scientists and engineers to transform prototypes into scalable, secure, reliable platforms. Lead technical delivery, make architecture tradeoffs, improve engineering standards, mentor teammates, and promote responsible AI practices.

Build and deploy production-grade AI and machine learning systems supporting biomedical research, including multimodal models, intelligent agents, retrieval, knowledge systems, evaluation frameworks, and inference services. Own architecture through production, make scalability and reliability tradeoffs, establish responsible AI practices, and drive technical delivery. Collaborate with scientists, computational biologists, and platform teams while mentoring engineers and improving engineering standards, observability, and system performance.

Design, develop, deploy, and support foundational AI systems, including LLM inference, multimodal safety guardrails, agents, similarity search, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, reliability, and hardware utilization. Lead architecture and technical reviews, define AI reliability objectives, ensure security and compliance, and mentor engineering staff.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization; define reliability objectives; own complex system architecture; lead technical reviews; ensure security and compliance; and mentor engineering associates.

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

Leads an AI engineering team developing scalable generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and agentic workflows. Sets technical vision, makes build-versus-buy decisions, optimizes production AI systems, operationalizes Responsible AI standards, develops team roadmaps, collaborates cross-functionally, and mentors engineering talent.

Design and build generative AI proofs of concept, pilots, reusable components, APIs, and production-ready reference implementations for commercial insurance. Apply RAG, AI agents, intelligent document processing, model integration, and structured outputs. Evaluate solutions for quality, safety, reliability, and business value while establishing secure, observable, scalable architectures. Partner with technical and business stakeholders, mentor engineers, communicate recommendations to leadership, and shape technical direction across initiatives.

Develop AI-enabled software solutions for assurance platforms and client applications. Design, code, test, troubleshoot, and maintain scalable Python and API-based applications using cloud, DevOps, CI/CD, LLM, prompt engineering, and RAG technologies. Translate business requirements into technical designs, document delivery decisions, collaborate with clients and teams, review code, resolve issues, and guide junior developers.

Leads the design, development, and operation of enterprise AI and machine learning platforms across public and private clouds. Builds MLOps infrastructure, model training and inference workflows, observability, governance, automation, and deployment capabilities. Provides technical leadership, resolves complex platform issues, influences architecture, partners with cross-functional teams, and mentors engineers. The role emphasizes scalable, secure, reliable AI operations, generative AI enablement, infrastructure as code, CI/CD, monitoring, and enterprise platform adoption.

Build and deploy scalable, responsible AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architectures, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to platform roadmaps, and mentor engineering staff.

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

Develop AI and data analytics solutions for AML and sanctions clients within PwC’s Risk & Regulatory practice. Responsibilities include Python-based data analysis, designing data architectures and pipelines, creating dashboards, interpreting complex datasets, engaging stakeholders, and delivering actionable insights and tailored client solutions. The role also involves client relationship building, problem-solving, continuous improvement, and contributing to strategic business outcomes.

Leads enterprise AI engineering strategy and multi-team execution. Oversees foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and production optimization. Makes technology decisions, scales reusable AI infrastructure and responsible AI practices, partners with research and risk teams, and develops engineering talent. Requires strong hands-on expertise in AI systems, cloud deployment, model optimization, and organizational leadership.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.