Top Senior Machine Learning Jobs in Houston, TX

The Engagement Manager manages project execution, collaborates with technical teams on AI solutions, develops client relationships, and coaches consultants.

The Engagement Manager leads client projects, manages teams, ensures timely delivery, develops client relationships, identifies business opportunities, and provides thought leadership.

The Director leads AI and emerging technology risk consulting, advising on governance, ethical use, and developing risk frameworks, while managing client engagements and sales strategies.

Lead and manage consulting engagements delivering power market analysis, price forecasts, asset valuations, transaction support, and strategic advisory. Oversee modeling (PROMOD, PSLF, PLEXOS, Aurora), mentor staff, manage client relationships, drive business development, publish thought leadership, and contribute to proprietary forecasting and analytical tools across U.S. power markets and technologies including renewables, storage, transmission, and large loads.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Support development of pricing arrangements, financial models, budgets and AFAs for a law firm. Produce pricing analyses, competitive market research, and performance metrics. Collaborate with attorneys, IT, and data teams to implement pricing tools (Elite 3E, Iridium), leverage AI/analytics for forecasting, and train stakeholders on pricing technology.

Lead and operate a 24x7 IT and OT Security Operations Center as a player/coach. Manage SOC staff and MDR relationships, oversee detection, investigation, and response across IT and industrial control systems, drive AI/ML and automation adoption, maintain playbooks and metrics, and collaborate with cross‑functional teams to improve security posture.

Design and build scalable, secure, high-performance systems using Python and AWS. Lead design/code reviews, enforce SDLC best practices, optimize with tools like AWS X-Ray, collaborate with stakeholders across lines of business, mentor junior engineers, and contribute to trade and risk management solutions while leveraging Generative and Agentic AI.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Own end-to-end client relationships for a book of B2B SaaS customers: drive adoption, engagement, retention, and expansion of AI-powered contract drafting tools. Lead onboarding, training, large-scale rollouts, and translate customer feedback into product improvements while designing scalable CS processes and tracking health metrics to demonstrate value.

Lead forecasting, supply and logistics planning to ensure on-time deliveries and cost efficiency. Use digital tools, AI/ML, and real-time job data to optimize supplies, monitor suppliers, adjust forecasts, and support field operations. Train and advise engineers, identify cost-saving opportunities, and develop improved workflows and machine-learning tools for the supply chain control tower.

Lead backend architecture and implementation for scalable, reliable cloud-hosted web products. Mentor engineers, set standards, integrate agentic AI coding tools, design systems applying ML to large datasets, and balance customer-facing features with technical debt and performance.

Lead end-to-end product areas that improve build speed, cost accuracy, margin, and throughput. Own strategy, roadmap, and execution for complex financial and operational data products, integrate AI/ML into workflows, write precise requirements, instrument metrics, run experiments, and drive cross-functional outcomes with construction, finance, sales, design, and engineering teams.

Design and scale fraud-mitigation strategies for buyer, seller, and account fraud across Stripe products. Monitor portfolios, identify and predict risky behavior, propose controls, automate workflows, collaborate with Product/Engineering/Data Science, and educate users to minimize losses while enabling growth.

Lead the Contracts product roadmap for a B2B healthcare SaaS platform, applying deep reimbursement and managed care expertise. Partner with AI engineers to build contract-understanding models, drive adoption, iterate on workflows, create business cases, and coordinate cross-functionally with Legal, Finance, Sales, and SMEs to ensure compliant, revenue-aligned contract execution.

Drive U.S. strategic growth by identifying, developing, and closing complex enterprise deals in the Databricks ecosystem. Build executive relationships, lead co-sell motions with partners, design account plans, and partner with Sales, Engineering, and Delivery to ensure successful execution and long-term value. Own CRM updates and relay market feedback to shape offerings and GTM strategy.

Lead the vision and execution of data product roadmap; translate business needs into epics and acceptance criteria; partner with data engineering and analytics; run agile ceremonies and manage backlog; champion data-as-a-product; scope and pilot AI/ML capabilities; present roadmaps and business cases to leadership.

Lead engineering team to define and execute threat detection, platform, analytics, and automation roadmaps. Advance automated, AI-augmented detection and response across SIEM/EDR/NDR/SOAR and cloud environments, partner with Incident Response, and drive measurable risk reduction via metrics and team development.

Lead design and delivery of a unified, real-time billing platform that embeds AI/ML to automate reconciliations, detect anomalies, enable predictive insights, and create autonomous finance workflows across global billing operations.

Embed with customers to discover needs, design and implement production-grade integrations (APIs, data pipelines, event-driven workflows), deploy and troubleshoot solutions, train IT teams, and drive adoption and measurable business outcomes.

Design and implement data-centric cybersecurity solutions: develop ML algorithms and automation for security tools; discover, parse, map, and transform legacy RMF artifacts into OSCAL; migrate and analyze security data; reproduce dashboards/workflows in eGRC; collaborate with Professional Services; produce migration assessments and reports.

Lead engineering on cloud-based AI/ML data platforms: design and ship secure production code, guide teams on AI-assisted development and SDLC automation, drive MLOps and DAG-based pipelines adoption, coach on responsible AI practices, and influence cross-team tooling and governance.

Lead an agile team to design, build, and deliver secure, scalable full-stack applications. Drive adoption of enterprise AI-assisted engineering, evaluate vendors, mentor engineers on responsible AI, and promote best practices across software development and cloud infrastructure.

Lead client-facing finance transformation engagements to optimize finance functions through operating model design, process improvement, automation and AI. Manage project teams and client relationships, deliver high-quality recommendations and tools, identify value-creation opportunities across G&A, support business development, and coach junior staff.

As a Senior Fullstack Engineer, you'll architect and build a high-concurrency auction engine, leveraging AI for enhanced user experiences and system reliability.