Top AI & Machine Learning Jobs

Paid summer data science internship supporting banking analytics through data querying, statistical analysis, customer behavior and profitability reporting, campaign analysis, experimental design, and presentation of findings to data scientists and business leaders. The role runs for 11 weeks on a full-time Monday-through-Friday schedule with a hybrid arrangement requiring two in-office days per week.

Build and optimize edge and cloud data pipelines for perception ML models. Scale data engine for high-fidelity labels, reduce annotation costs, integrate foundation models for automated labeling and QA, leverage software/hardware-in-the-loop testing, and support DoD field use cases and model deployment lifecycle.

Lead the engineering team building Chariot’s AI claims platform, including agent systems for case origination and claims matching, human-in-the-loop review, evaluation infrastructure, and scalable AI performance. The role owns product decisions, internal tooling, cost efficiency, and production reliability while recruiting and developing full-stack and ML engineers. Experience with production software, end-to-end product ownership, early-stage startups, and modern LLM systems is valued.

Build, scale, and operate production-grade machine learning systems supporting real-time personalization. The role involves developing scalable data analysis and model-development processes, validating and implementing models, protecting system quality through testing, collaborating cross-functionally, and leading machine learning initiatives. The engineer will work extensively with Python, modern ML frameworks, large-scale data systems, and AWS-based infrastructure while providing technical leadership.

Design, develop, and operationalize AI and machine learning solutions for production applications and data workflows. Build data and model pipelines, implement MLOps and LLMOps practices, support generative AI and RAG systems, and contribute to CI/CD, observability, responsible AI, governance, and model lifecycle management. Collaborate with data science, software engineering, and cloud teams in secure federal environments.

Develop backend SaaS and multi-tenant applications for retail planning using Python, Java, Spring Boot, and Spring Cloud. Apply forecasting, machine learning, and generative AI techniques to supply chain planning use cases. Design scalable APIs, integrate external systems, manage SQL and NoSQL data, improve performance, and support customer escalations. Collaborate with stakeholders, QA, and account teams while contributing to architecture, testing, observability, and continuous product improvement.

Leads a span of approximately 20 engineers while shaping hiring, onboarding, career development, staffing, and engineering enablement across the AI practice. The role is also approximately 50% billable client delivery, providing executive advisory on AI software development lifecycles, governance, agentic deployments, MLOps and LLMOps pipelines, cloud platforms, observability, and production resilience. Requires extensive engineering leadership, organizational scaling, consulting, GCP, and production AI/ML deployment experience.

Lead product strategy and roadmap for AI Ready Data capabilities—data ingestion, transformation, enrichment, synthetic data, and tooling for retrieval, fine-tuning, evaluation, and agent workflows. Write product specs, partner with engineering, research, design, and GTM, translate experiments into scalable features, work with customers to surface requirements, and define success metrics to monitor adoption and performance.

Lead presales engagements to uncover business drivers and design responsible, scalable AI solutions on the Microsoft cloud. Run enterprise discovery workshops, shape measurable outcomes, architect across Azure Data/AI/Security/Integration/Power Platform, produce SOWs and phased delivery plans, and communicate value to executive stakeholders while partnering with sales and delivery for deal readiness and risk mitigation.

Lead end-to-end post-training of open-weight language models on proprietary legal data, including dataset development, objective design, supervised fine-tuning, preference optimization, reinforcement learning, evaluation, and distributed training. Build production model-serving systems, agents, tooling, and infrastructure. Partner with product engineers, lawyers, and domain experts to translate workflows into model and evaluation strategies and deliver AI capabilities to users.