Top AI & Machine Learning Jobs

Develops NLP and machine learning solutions for VA disability claims, extracting and structuring medical and claims data. Builds and maintains NLP pipelines, tunes transformer models, validates accuracy, documents workflows, collaborates with VA stakeholders, and integrates models into government systems. Requires healthcare or claims data experience, cloud familiarity, and the ability to communicate technical concepts effectively.

Develop, test, deploy, and maintain AI applications, agentic tools, MCP servers, containerized LLMs, and RAG knowledge bases. Support AI infrastructure administration, model testing, monitoring, documentation, training, security vulnerability remediation, HIPAA compliance, and agile collaboration. The role is onsite in Montgomery, Alabama, beginning on day one.

Build Pelico’s agentic platform end to end, developing Python backend services, React interfaces, agent tools, orchestration workflows, runtime reliability features, evaluation harnesses, and real-time approval consoles. The role involves shipping production AI capabilities, improving agent trustworthiness, defining technical specifications, and creating reusable full-stack engineering patterns for manufacturing customers.

Build and deploy production AI applications for enterprise clients, using machine learning, LLMs, retrieval systems, agents, cloud technologies, APIs, and modern software engineering practices. Partner with stakeholders to translate ambiguous business challenges into scalable solutions, lead projects from discovery through adoption, optimize performance and reliability, and contribute reusable frameworks and technical strategy. The role is remote with approximately 30% client travel.

Develop scalable AI applications, integrate and orchestrate LLM workflows, engineer prompts, implement RAG, classify technical data, and build AI-powered developer tools such as VS Code extensions and command-line utilities. The role also requires Git collaboration, external data-source integration, monitoring, guardrails, data preprocessing, feature engineering, and classifier validation.

Develop AI/ML and computational genomics methods for trait discovery, genomic prediction, and crop breeding decisions. Build scalable workflows integrating genomic, phenotypic, expression, pathway, and literature data. Apply graph machine learning, foundation models, knowledge graphs, and explainable AI while validating reproducibility and scientific rigor. Collaborate with geneticists, breeders, biologists, engineers, data scientists, and product teams to translate research questions into practical decision-support tools.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy-farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and integration with cloud infrastructure. The role also drives AI adoption, establishes engineering practices, collaborates cross-functionally, and may build React and TypeScript user experiences.

Leads the architecture, development, deployment, and optimization of enterprise AI solutions combining Neo4j knowledge graphs, LLMs, and generative AI frameworks. Advises strategic customers, translates business needs into scalable production applications, guides stakeholder alignment, and oversees technical delivery. Develops production-level code, integrates customer systems, and applies cloud-native deployment practices. Shares expertise through workshops, training, documentation, and thought leadership while influencing product direction and mentoring teams.

Twelve-week data science internship involving SQL, Python, Power BI, cloud analytics, dashboards, models, and causal inference. The intern will analyze business problems, collaborate with analytics teams and leaders, communicate findings, participate in professional development, and present project outcomes to senior leadership.

Supports enterprise AI governance by administering risk-tiering and intake workflows, maintaining governance data and documentation, tracking exceptions and remediation, monitoring lifecycle reviews, and developing dashboards and reporting. Partners with business, technology, security, legal, and compliance teams to support audits, improve controls, maintain AI governance policies, and enable responsible AI adoption.