Top AI & Machine Learning Jobs

Build and lead the technical direction of JLL’s internal AI platform, including a multi-provider model gateway, agent infrastructure, evaluation and benchmarking, guardrails, observability, cost optimization, and latency management. Own production reliability, APIs, abstractions, incident response, and platform performance. Mentor distributed teams, guide architecture and responsible-AI compliance, and help internal engineering teams deploy AI agents and products safely at scale.

Lead end-to-end Litify AI implementations for legal clients. Design Salesforce-based AI agent workflows, conduct requirements discovery, configure and deploy solutions, advise clients, define success metrics, and drive adoption. Partner with project managers, sales, engineering, customer success, and product teams while owning technical architecture, documentation, delivery outcomes, and client feedback. The role requires strong Salesforce implementation expertise, AI agent and LLM experience, prompt engineering, and client-facing consulting skills.

Lead the architecture, development, and deployment of scalable AI-powered backend systems. Design APIs and inference workflows, fine-tune and productionize LLM and transformer models, establish MLOps and governance standards, and evaluate AI technologies. Mentor engineers, guide cross-functional technical initiatives, improve system reliability and performance, and shape the organization’s AI strategy and roadmap.

Use statistical, algorithmic, and visualization techniques to model business problems, derive actionable insights from member data, and inform product and service decisions. Collaborate with end-users, build analytic capabilities, communicate data-driven recommendations, and promote advanced analytics adoption across the organization.

Build and maintain AI-powered workflows, agents, and lightweight tools that improve sales, marketing, and RevOps efficiency. Automate outreach, lead enrichment, personalization, HubSpot operations, reporting, attribution, and recurring analysis. Partner with GTM teams to identify repetitive processes, prototype scalable solutions, document systems, and advise on AI applications. The role requires hands-on experience building automations, internal tools, scripts, or workflows using LLMs and low-code platforms.

Conduct research on deep multimodal perception models for autonomous driving. Responsibilities include analyzing model representations, developing active learning and data curation algorithms, creating automated VLM labeling workflows, training and evaluating neural networks, and validating performance using large-scale vehicle sensor datasets and simulation environments.

Leads enterprise AI and automation deployments from validated prototypes through secure, supported production. Owns the operating relationship between AI and IT, including governance, security, architecture standards, data access, vendor strategy, monitoring, support, and operational resilience. Manages technical teams, budgets, staffing, career development, and deployment across operating brands while evaluating emerging AI technologies and recommending practical business applications.

Lead client engagements using data science, AI, advanced analytics, statistical modeling, experimentation, and marketing measurement. Manage and develop analysts and data scientists, advise quantitative researchers, pitch projects, grow client relationships, and provide thought leadership on machine learning and emerging AI methods. Translate technical work into strategic recommendations for clients and executives.

Build and improve an autonomous AI coding agent that understands tasks, generates and tests code, self-repairs, reviews changes, and integrates with developer tools. Design codebase retrieval and knowledge systems, build scalable Python services and APIs, leverage LLMs, develop agent evaluations, and identify automation opportunities with platform and product teams. The role requires strong software engineering, cloud infrastructure, CI/CD, testing, and end-to-end ownership.

Architects and ships production agentic AI systems, including multi-agent orchestration, RAG pipelines, evaluation infrastructure, and observability. Establishes reusable technical standards, makes architecture and build-versus-buy decisions, mentors engineers, and communicates technical choices to executives and clients. The role requires hands-on Python and TypeScript/React development, rapid delivery of ambiguous client solutions, and frequent use of AI coding tools. Client-site travel is required as needed.