Top AI & Machine Learning Jobs

Build and operate secure, scalable AI/ML platforms across hybrid and on-premises environments. Develop data pipelines, MLOps workflows, Kubernetes deployments, CI/CD and GitOps automation, observability, infrastructure-as-code, and secure container supply chains. Support model serving, generative AI, and retrieval-augmented generation while troubleshooting distributed systems, improving reliability, and contributing to architecture, standards, documentation, incident response, and technical mentorship.

Develop secure, source-grounded AI applications using retrieval-augmented generation, multimodal document intelligence, explainability, citations, evaluation frameworks, and human feedback workflows. Build Azure-native multi-tenant applications with PostgreSQL, enterprise RBAC, hybrid retrieval, and auditability. Collaborate in Agile teams, engage customers, mentor engineers, and support continuous improvement of prompts, models, retrieval systems, and evaluation processes.

Designs, develops, tests, validates, and deploys AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Builds data and model-training pipelines, leverages cloud platforms, evaluates model performance, documents methodologies, collaborates with technical teams, and guides prototype-to-production transitions. Requires five years of relevant experience and an active TS/SCI clearance with polygraph.

Design, build, and deploy machine learning solutions in distributed systems; collaborate with engineers and subject-matter experts; evaluate and improve models; ensure data security and compliance; communicate insights to stakeholders; mentor junior data scientists.

Analyze complex datasets to deliver actionable insights for production, resource allocation, and personnel decisions. Develop Tableau dashboards and visualizations, preprocess and clean structured and unstructured data using Python or R, and analyze time-series data. Collaborate with stakeholders, ensure data quality, document methodologies, and provide recommendations. The role requires an active TS/SCI with Polygraph clearance and is onsite in Northern Virginia.

Design and build distributed data systems for analysis and ML; lead and mentor data scientists; implement, deploy, and improve ML models (including deep learning and LLMs); ensure data quality, security, and compliance; integrate models into customer on‑prem and cloud environments; communicate insights to stakeholders and drive best practices.

Lead data science efforts for a defense program onsite in Hazelwood, MO. Define data requirements, build analysis systems, design/deploy ML models, collaborate with engineers and SMEs, mentor junior scientists, ensure security/compliance, and communicate results to stakeholders while improving models and establishing best practices.

Provides technical leadership in mathematical research, numerical algorithm development, neural network design, machine learning applications, and large-scale data processing. Develops algorithms, prepares actionable metrics, supports simulation validation and correlation studies, and collaborates with multidisciplinary engineering teams. Produces technical reports, specifications, design documentation, and customer briefings. The role is fully onsite in Berkeley, Missouri, includes up to 10% travel, and requires obtaining a U.S. Top Secret clearance and Special Program Access.

Senior Data Scientist analyzing large-scale unstructured and semi-structured data using NLP, latent semantic indexing, exploratory analysis, machine learning, and clustering. The role researches novel approaches for distributed computing applications, visualizes complex results, and delivers recommendations for business decisions. Responsibilities include collaborating on data and intelligence platform improvements and using Python, R, SQL, Pandas, Jupyter, APIs, and visualization tools. The position requires extensive experience and an active high-level security clearance.

Designs and leads enterprise AI architectures from proof of concept through production, including data ingestion, model training, LLM/RAG pipelines, agentic workflows, deployment, monitoring, and lifecycle management. Evaluates AI technologies, establishes strategy and standards, develops low-code and no-code automation layers, builds MVPs, and coordinates with executive, data science, IT, service tower, vendor, and consulting stakeholders. The hybrid role requires up to 20% domestic travel and U.S. Person status for export-control compliance.