Top AI & Machine Learning Jobs

Lead fraud operations automation by building AI agents and LLM workflows, identifying manual investigative processes, and converting them into auditable automated workflows. Provide analytical insights into suspicious merchants and customers, develop prioritization methods, and maintain operational dashboards and KPIs. Partner with Fraud Strategy, Product, Data, and Engineering teams to improve fraud policies, models, case management, and investigator effectiveness.

Designs, deploys, and maintains AI-driven web applications, services, and agentic workflows using Anthropic APIs and Claude. Develops Python REST APIs and React/TypeScript front ends, creates prompt and LLM evaluation frameworks, and manages testing, debugging, security, observability, CI/CD, Docker, and cloud deployment. Optimizes SQL queries and collaborates with business teams to deliver reliable, cost-effective AI solutions.

Build and operate real-time voice AI agents for Call Guard. Responsibilities include designing conversational systems, reducing latency across speech and language components, integrating frontier models and agent frameworks, defending against voice-cloning and adversarial attacks, and maintaining telephony, VoIP, and cloud infrastructure. The role also involves architectural ownership, scalable backend development, cross-functional product collaboration, code reviews, and mentoring while delivering privacy-focused consumer products.

Builds and deploys end-to-end generative AI solutions, translating business needs into scalable, secure, and maintainable architectures. Responsibilities include AI/ML observability, LLM gateways, cost controls, rate limiting, security guardrails, self-service deployment frameworks, CI/CD integration, and containerized ML workloads. The role collaborates with infrastructure and cross-functional teams while evaluating emerging AI capabilities and supporting enterprise AI initiatives.

Develops production-grade statistical and machine learning models and predictive solutions for healthcare operations and business performance. The role analyzes complex data, designs experiments, evaluates AI systems, establishes metrics, creates dashboards, standardizes reporting, and communicates insights to technical and executive stakeholders. It also supports generative AI evaluation, model deployment workflows, and data-driven decision-making in regulated industries.

Build and own a scalable API platform that enables external organizations to upload data, fine-tune and evaluate AI models, and run low-latency inference for robotics applications. Responsibilities include designing multi-tenant infrastructure, APIs, deployment integrations, observability, reliability, and developer tooling. The role requires strong backend and systems engineering skills, Python expertise, production machine learning familiarity, and end-to-end ownership of ambiguous platform problems.

Build and optimize production AI pipelines for healthcare workflows using LLMs, classical ML, VLMs, OCR, retrieval, agents, and fine-tuned models. Develop document extraction systems, evaluation harnesses, monitoring, and inference orchestration. Improve model quality, latency, cost, explainability, and safety while collaborating with product, engineering, senior ML engineers, and healthcare domain experts.

Supports enterprise data modeling, integration, quality improvement, and machine-learning projects in a manufacturing environment. Responsibilities include product-family classification using BOM data, feature development, model testing, validation, MLOps and AI research, agentic workflow mapping, controlled AI testing, documentation, and communicating findings to technical and business stakeholders. The intern collaborates across departments while gaining practical experience in data engineering, machine learning, and AI-enabled process improvement.

Develops and deploys data science solutions for information operations and national security customers. Responsibilities include statistical modeling, machine learning, deep learning, simulation, network analysis, multilingual text analytics, data visualization, experimentation, prototyping, customer engagement, and technical translation. The role also supports deployment in cloud, containerized, and air-gapped environments, including configuration, monitoring, troubleshooting, and maintaining customer systems. An active Top Secret clearance with SCI eligibility is required, and the position is onsite in the DC Metro area up to four days weekly.

Designs, develops, and deploys enterprise Generative AI solutions using LLMs, semantic search, and AI orchestration. Builds monitoring systems, improves AI architecture, supports prompt engineering and evaluation, reviews code, and mentors engineers. Collaborates with product leaders and stakeholders to deliver secure, scalable, customer-facing AI applications. Requires Azure OpenAI, Azure AI Search, Semantic Kernel, DevOps, CI/CD, testing, and advanced Python expertise.