Top AI Engineer Jobs

Set technical direction for AI-powered developer tools and a self-service AI platform. Design, build, and operate production AI agents, scalable distributed systems, deployment frameworks, observability integrations, security controls, and CI/CD integrations. Establish LLM and agent-development standards, lead cross-team initiatives, mentor engineers, conduct design reviews, measure AI-tool effectiveness, and participate in on-call operations.

Develop data-intensive and AI-enabled applications supporting product development engineering teams. Build optimized SQL solutions, Databricks pipelines, Java and Python backend services, APIs, and cloud-native applications. Partner with AI teams to productionize ML and LLM solutions, improve reliability and security, troubleshoot production issues, and implement engineering best practices. The role also involves architecture, Agile collaboration, mentoring engineers, technical documentation, and driving modernization initiatives.

Leads AI and data-driven solutions for AML and sanctions clients in banking and capital markets. Responsibilities include developing AI solutions with Python, managing data architecture and pipelines, overseeing analytics and visualization deployments, guiding advanced analytics teams, engaging stakeholders, validating client outcomes, improving processes, and coaching diverse teams through complex situations.

Develop and improve reliable LLM and deep learning capabilities, build scalable real-time machine learning models, advance Responsible AI practices, and define technical strategies for deep learning, RAG, and LLMs. Collaborate with AI scientists and engineers, conduct ML and reinforcement learning research, fine-tune models, and drive adoption of AI best practices across service teams.

Lead the technical and product direction for a production conversational AI customer agent. Build and scale agentic systems covering orchestration, evaluation, guardrails, memory, context, reliability, and failure handling. Remain hands-on with coding, architecture, debugging, and production operations while mentoring engineers and partnering with product, design, data science, and go-to-market teams. The role is based in-office in Boston and may require up to 10% travel.

Lead the technical and product direction for a production conversational AI customer agent. Build and scale agentic systems involving orchestration, evaluation, guardrails, memory, context, reliability, and failure handling. Write and review code, make architecture decisions, collaborate cross-functionally, mentor engineers, evaluate emerging AI technologies, and support systems through launch and live customer usage.

Lead AI initiatives for Human Capital by identifying high-impact use cases, designing Python-based prototypes, integrating AI features with product and engineering teams, evaluating AI tools, shaping AI strategy, and mentoring junior engineers. The role requires translating business needs into practical solutions and collaborating across technical and non-technical stakeholders.

Lead architecture and implementation of a patient-controlled health data platform: custody, consent management, secure EHR integrations, AI-native workflows (LLMs, agents, RAG), mobile-first patient experiences, and build the engineering culture and hiring bar as a founding technical partner.

Lead AI Engineer defines AI strategy, architecture, and standards; makes build vs. buy decisions; architects scalable, reliable LLM/GenAI systems; establishes LLMOps, evaluation, guardrails, and observability; coordinates cross-functional alignment with product and engineering; and champions AI-assisted developer tooling and responsible AI practices.

Lead AI coding-tooling standards and developer experience, define Skill Marketplace conventions, and build production-quality internal AI application prototypes. Develop reference implementations for agent workflows, streaming UX, memory, multi-agent collaboration, and AI-native engineering processes. Provide architecture guidance, review code, publish technical content, deliver workshops, and collaborate asynchronously with APAC teams. The role requires strong full-stack engineering, product judgment, LLM application experience, and public technical influence.

Lead AI Engineer responsible for guiding AI engineering teams and designing scalable machine learning systems. The role includes distributed training, deep learning, LLM fine-tuning and serving, agentic AI applications, model optimization, cloud deployment, monitoring, and collaboration with data science and engineering teams. The position requires technical leadership, production Python development, distributed computing, cloud ML platforms, and extensive experience with modern machine learning frameworks.

Leads software engineering and AI initiatives for enterprise-scale banking technology. Designs scalable frameworks, writes and reviews production code, and establishes secure, auditable agentic AI development workflows. Defines responsible AI guardrails, target-state architecture, reusable engineering practices, and cloud-native solutions. Advises cross-functional teams, influences senior stakeholders, serves as a technical subject matter expert, and collaborates on product and engineering roadmaps.

Lead the design, development, and deployment of scalable AI and machine learning solutions. Responsibilities include distributed model training, hyperparameter optimization, LLM fine-tuning and serving, deep learning performance optimization, ML platform integration, model monitoring, and technical leadership of AI engineers. The role collaborates with data science, data engineering, technology teams, and leadership to deliver production-grade AI products.

Leads AI Developer Experience and AI Business Enablement teams. Owns AI-assisted development workflows, agent context, testing infrastructure, cloud environments, CI, code review, and secure tools for nontechnical builders. Sets strategy, manages managers, hires teams, evaluates build-versus-buy decisions, drives adoption, and measures improvements in delivery speed, software reliability, time savings, and sustained tool usage.

Lead the evolution of trading infrastructure by managing trade operations, P&L attribution, risk monitoring, data integrity, and performance reporting. Develop Python- and machine-learning-based AI agents, MLOps workflows, anomaly detection, predictive risk models, automated reconciliations, and real-time dashboards to transform manual trading operations and improve risk analysis.

Builds and operates production AI applications for commercial teams. Responsibilities include developing generative AI, agent, machine learning, and RAG solutions; integrating AI with Salesforce, CRM, ERP, pricing, and quoting systems; creating data pipelines and reliable APIs; monitoring model quality, bias, drift, and performance; defining evaluation and release criteria; applying security controls; supporting production solutions; and mentoring engineers.

Builds and deploys production AI solutions for supply chain, procurement, quality, and engineering operations. Develops agents, copilots, RAG solutions, model-driven tools, pipelines, and MLOps/LLMOps workflows integrated with ERP, planning, EDI, and manufacturing systems. Partners with technical and business teams to define use cases, evaluate models, monitor performance, drive adoption, and improve reliability, cost, latency, and explainability. Mentors engineers and contributes to architecture and technical standards.

Designs, develops, deploys, and operates production AI solutions across commercial, engineering, quality, operations, supply chain, and procurement functions. Builds generative AI, agent, copilot, RAG, machine learning, and computer vision applications; integrates them with enterprise systems; and creates data pipelines, MLOps workflows, reusable frameworks, and monitoring practices. Owns solutions from prototype through production, applies security controls, evaluates performance and business impact, collaborates with stakeholders and vendors, and mentors engineers.

Build production-grade AI applications, agents, data products, and intelligent workflows using Python and enterprise platforms. Responsibilities include agent orchestration, retrieval, tool integration, model hosting, platform engineering, CI/CD, security, observability, evaluation, and production validation. The role requires integrating AI into enterprise and investment workflows while designing scalable, resilient, and secure services across cloud, container, and distributed-system environments.

Own the AI and intelligence layer for a finance platform, including customer-specific ML models, anomaly detection, LLM and agentic systems, RAG, grounding, evaluation, and hallucination controls. Build production-grade Python services, ensure leakage-free modeling and faithful outputs, deploy across customer cloud environments, and provide technical leadership while mentoring engineers and partnering with product, data, backend, and evaluation teams.

Designs and ships production multi-agent AI systems for KYB, underwriting, case review, and risk monitoring. Owns agent architecture, retrieval, tool integrations, evaluations, observability, MLOps, reliability, and compliance guardrails. Requires strong Python engineering, agent framework expertise, distributed systems experience, Kubernetes operations, and production LLM deployment skills. Mentors engineers and partners with product, security, compliance, and applied science teams. Remote employees travel to Orlando at least twice annually.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Build AI-driven products spanning autonomous agents, model evaluation, synthetic training data, vector search, web crawling, inference APIs, backend infrastructure, and user-facing web applications. The role also develops data-management tools and engaging game-like features. Candidates should have experience shipping end-user AI products, building LLM applications, and delivering full-stack systems in an early-stage environment.

Build and operate DAT’s shared AI platform, including model gateways, inference services, feature and vector storage, evaluation systems, observability, and agentic frameworks. Architect secure, scalable AWS infrastructure; optimize model cost and latency; implement governance, monitoring, and quality controls; establish engineering standards; lead incident response; and mentor engineers and data scientists. The role requires technical leadership across teams and production experience with distributed systems, LLMs, machine learning platforms, and cloud infrastructure.

Build backend services, AI agent runtimes, machine learning infrastructure, vector search systems, data acquisition pipelines, model-hosting endpoints, quality measurement workflows, and internal platform tooling. The role also includes security assessments and vulnerability mapping. Candidates should be comfortable solving ambiguous problems independently, developing dataset and synthetic-data pipelines, and maintaining high standards for simple, dependable engineering.