Top AI Jobs in Boston, MA

Leads advanced analytics, machine learning, and AI initiatives for Finance, developing predictive models, forecasting solutions, optimization, automation, anomaly detection, and conversational analytics. Partners with Finance, Data, Technology, and Engineering teams to translate business challenges into scalable solutions, establish governance and testing frameworks, evaluate model performance and bias, and drive adoption. Provides technical leadership and mentorship while supporting Finance transformation and continuous improvement.

Leads AI and blockchain solution architecture for client and internal initiatives. Designs scalable architectures integrating AI, blockchain, cloud, DevOps, APIs, and automation; translates business requirements into technical designs and roadmaps; evaluates technologies and proof of concepts; manages risks and dependencies; guides delivery teams; communicates architecture decisions; and coaches architects and developers.

Leads enterprise data science and AI solution delivery, including ML, generative AI, and predictive model development, deployment, monitoring, governance, and integration into business workflows. Drives AI adoption, change management, value realization, and evaluation of emerging technologies while partnering with data, engineering, and platform teams. Builds and leads AI delivery teams and ensures compliance with security and governance standards.

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.

Leads client-facing AI adoption and productivity initiatives, designing Azure-based generative AI solutions, prompts, agent workflows, and workforce analytics models. Oversees LLM evaluation, guardrails, governance, deployment, and adoption measurement. Manages engagement workstreams, budgets, timelines, deliverables, client relationships, and team coaching while guiding training and AI-human collaboration programs.

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 a team of data scientists while remaining hands-on in designing, building, and evaluating GenAI solutions and agentic workflow automation. Develops AI evaluation metrics, MLOps capabilities, underwriting decision tools, and reusable AI products. Partners with engineers and business stakeholders, communicates technical findings, advises complex projects, and mentors staff to scale AI-enabled commercial underwriting and claims workflows.

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.

Build and operate production AI-enabled healthcare products end to end, including backend services, data pipelines, EHR integrations, AI agents, evaluation systems, cloud infrastructure, and user interfaces. Own deployment, observability, security, reliability, and technical architecture for systems used by patients and care teams. Partner with product, clinical, design, operations, legal, privacy, and engineering teams while establishing development, evaluation, documentation, and operational standards.

Conduct applied AI research focused on foundation models, evaluation, and agentic workflows. Build, train, validate, and deploy large-scale deep learning models using technologies such as PyTorch, AWS, Hugging Face, and Lightning. Lead research initiatives, develop platform and solution-level code, scale models for production, collaborate with engineering and product teams, and translate research findings into customer-facing AI products.

Lead enterprise AI engineering strategy and teams building scalable agentic AI platforms. Oversee foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and AI operations. Drive build-versus-buy decisions, optimization, responsible AI practices, multi-year technical roadmaps, and cross-functional alignment while developing senior engineering talent and supporting hundreds of production use cases.

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.

Lead AI product development from ideation through launch and scale. Partner with insurance carriers to identify communication challenges, test AI solutions, define strategy, establish trust, and manage success metrics and pricing insights. Coordinate engineering, design, Customer Success, Support, Sales, and Marketing to drive adoption. The role requires building generative AI products, ideally conversational agents, in a hybrid Boston-based environment.

Research and develop real-time AI algorithms for audio source separation, target sound extraction, and speech enhancement. Train and evaluate PyTorch models using single- and multi-microphone recordings, spatial cues, and auxiliary sensors. Address overlapping speakers, noise, reverberation, motion, perceptual quality, latency, memory, and computational constraints. Collaborate with DSP and embedded software teams to deploy models on physical devices, while contributing to patents, publications, and product demonstrations.

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.