Top AI & Machine Learning Jobs

Develops data science, AI, machine learning, NLP, statistical, and visualization solutions for government audit and investigative work. Analyzes structured and unstructured data to identify fraud, waste, and abuse; builds and maintains models and analytical products; uses cloud data environments and relational databases; documents methodologies and technical requirements; communicates findings to stakeholders; and supports training, project collaboration, and agency mission objectives.

Designs and builds enterprise AI agents, Power Platform applications, and automation solutions with business stakeholders. Establishes architecture standards, reusable components, ALM practices, and platform guardrails; owns Dataverse architecture for Dynamics CRM and Dynamics 365 F&O. Leads agent grounding and integration architecture using RAG, MCP, A2A, knowledge sources, enterprise search, connectors, and APIs. Provides technical direction, code reviews, quality standards, and mentorship to a distributed India Center of Excellence.

Leads a portfolio of enterprise applied AI initiatives from strategy and prioritization through production deployment and continuous improvement. Oversees generative AI, agentic AI, machine learning, architecture, evaluation, governance, observability, and responsible AI practices. Manages multidisciplinary teams, establishes reusable platforms and technical standards, guides cloud and technology decisions, and partners with executives, business teams, security, compliance, vendors, and technical stakeholders to deliver reliable, secure, measurable AI capabilities.

Build and deploy AI agents for enterprise customers from discovery through implementation. Map business processes, connect systems, structure data, test and iterate agents, drive adoption, and measure business impact. Collaborate with executives, operators, Customer Success, Education, Product, and Engineering. Travel to customer sites as needed, up to 30%, while developing reusable deployment patterns and product feedback.

Develop statistical methodologies and production data science solutions for consumer purchase panel products. Apply sampling, weighting, modeling, classification, anomaly detection, and optimization to improve data quality and representativeness. Build scalable Python and SQL workflows, monitoring frameworks, and internal tools. Collaborate with Product, Engineering, Data Operations, and business partners throughout the project lifecycle, while clearly communicating methodologies, findings, assumptions, and tradeoffs.

Leads the global agentic engineering function, defining architectures, orchestration patterns, agent registries, evaluation systems, and AI-enabled software development practices. Establishes automated quality engineering, testing, governance, risk controls, and human oversight for autonomous systems. Builds and leads engineering teams, drives organizational transformation, partners with compliance and security stakeholders, and communicates complex AI capabilities to executives and broader technology teams.

Develop and deploy production-grade AI/ML solutions on Microsoft Azure. Build secure data pipelines, prepare and validate datasets, support NLP and machine learning workloads, and implement MLOps practices including CI/CD, monitoring, troubleshooting, and model lifecycle management. Collaborate with engineers, analysts, and stakeholders in Agile teams while producing architecture, testing, security, and operational documentation for regulated government environments.

Lead AI enablement for investment teams by translating LLM and generative AI capabilities into practical workflows, training and coaching investors, creating reusable prompt libraries and playbooks, guiding agentic designs, partnering with engineers and product teams, and ensuring responsible, secure adoption with measurable outcomes.

Build and deploy AI agents, automations, and tools that transform organizational processes. Embed with teams to map workflows, deliver time-boxed solutions using Claude and related platforms, measure business value, document and hand off production-ready systems, troubleshoot during stabilization, and create reusable patterns. Partner with AI Platform Operations, IT, risk, and staff while ensuring responsible handling of sensitive data and teaching teams to maintain solutions.

Own Alpaca’s growth data strategy across acquisition, activation, retention, referral, and monetization. Build experimentation frameworks, causal and incrementality analyses, funnel and cohort models, LTV/CAC and churn forecasts, marketing measurement, governed growth metrics, and self-serve analytics. Partner with Product, Marketing, Sales, Engineering, and Finance to influence strategy and roadmaps, communicate insights to leadership, mentor data professionals, and establish standards for growth analytics.