Top AI & Machine Learning Jobs

Leads data engineering, Palantir Skywise architecture, and AI solution development for Airbus manufacturing and operational environments. Builds scalable pipelines, analytical workflows, machine learning and generative AI applications, and enterprise data foundations. Oversees Alteryx and Tableau platforms, establishes governance, mentors technical teams, translates business challenges into data products, and supports AI Center of Excellence initiatives. The role is onsite 60% across several U.S. locations and requires approximately 10% travel.

Design, build, and deploy enterprise-grade LLM and Generative AI solutions (RAG, agents, vector DBs, semantic search). Architect APIs, integrations, and production pipelines; evaluate emerging AI tools; implement governance, security, observability, and reusable AI platform components to drive business adoption.

Advises clients on applying Microsoft 365 Copilot and related AI tools to business processes. Identifies use cases, designs Copilot solutions and agents, leads readiness and envisioning workshops, supports governance and change management, develops training content, and tracks adoption and ROI. Partners with technical teams, sales, and client stakeholders to translate AI capabilities into measurable productivity, cost, risk, and operational benefits.

Analyzes and reconciles data across systems, builds and maintains data pipelines, develops predictive and machine-learning models, and creates automated AI workflows for security intelligence, anomaly detection, and reporting. The role also develops LLM prompts, evaluates model quality, supports cloud and containerized infrastructure, contributes to CI/CD automation, monitors controls, creates SOPs, and provides analytical guidance to clients and management.

Designs, builds, and operates cloud-native infrastructure for AI and GenAI workloads across Azure and AWS. Responsibilities include GPU and Kubernetes platforms, networking, storage, model serving, RAG infrastructure, observability, CI/CD, infrastructure as code, security, compliance, reliability, and FinOps. The role defines AI infrastructure architecture and standards, leads solution design, operates platforms using SRE practices, creates reusable platform components, and mentors engineers.

Lead the design, testing, governance, and implementation of AI-assisted workflows across Finance, Tax, Treasury, and Risk. Responsibilities include prompt and agent logic development, process mapping, requirements gathering, AI output validation, automation documentation, stakeholder collaboration, production transition, troubleshooting, and training. The role supports scalable, compliant finance transformation using tools such as Microsoft Copilot, Claude, Alteryx, and UiPath.

Lead enterprise delivery of Databricks-based data, machine learning, and Generative AI solutions. Design production data pipelines, analytics engines, RAG applications, and ML platforms; migrate legacy data estates to the Lakehouse; and advise executive client stakeholders. Drive platform adoption, identify expansion opportunities, mentor technical teams, and develop reusable engineering accelerators. The role combines hands-on architecture, engineering, consulting, customer engagement, and technical practice building.

Provide data analysis, graphics, and dashboards to identify challenges and opportunities. Use Python or R, SQL, relational and NoSQL databases, Tableau, and time-series analysis to transform unstructured data into actionable insights supporting customer decisions about production, resources, and personnel.

Own sales forecasting and revenue analytics by developing statistical and machine learning models across products, regions, and time horizons. Lead the full machine learning lifecycle from feature engineering through deployment, monitoring, and explainability. Translate forecasts into business recommendations, deliver production data products, mentor junior data scientists, and provide technical leadership across analytics and sales teams.

Build and productionize internal AI applications from business-unit prototypes. Responsibilities include auditing AI tools, refactoring applications for security and scalability, integrating LLMs with Salesforce and the enterprise data lake, maintaining deployed solutions, managing stakeholders, and enforcing data governance, access controls, and hallucination mitigation.