Top Machine Learning Jobs in Baltimore, MD

Design, develop, and scale software systems for machine learning algorithms powering personalization. Collaborate with researchers and product teams to implement production-ready, distributed solutions, enable offline experiments and A/B tests, and improve engineering practices and systems across teams.

The role involves defining and refining Guidewire's AI narrative, crafting audience-specific messages, leading competitive positioning, and creating technical marketing assets to convey the value of AI-driven solutions in the P&C insurance sector.

The AI and ML Engineer will develop and maintain AI/ML models, collaborate with teams to deliver solutions for national security, and apply algorithms.

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

As a Machine Learning Engineer, you will fine-tune transformer models, deploy machine learning solutions, and work with cross-functional teams on innovative projects.

Integrate LLMs, RAG, and agentic-AI into secure decision-support software. Design prompts and inference workflows, connect AI to backend services, evaluate outputs, build tests, document designs, and collaborate with engineering, security, and DevSecOps teams to deliver reliable mission-capable AI features.

Lead a team of ML engineers to design, build, and productionize LLM/GenAI and agentic systems (retrieval, tool use, orchestration). Collaborate cross-functionally with MLOps, product, and data science, review code, mentor engineers, run technical evaluations and proofs-of-concept, and ensure reliable, safe ML deployments for a large SaaS product.

The Staff ML/AI Platform Engineer will design, prototype and deploy AI/ML models, optimize pipelines, mentor junior engineers, and collaborate closely with other teams to develop machine learning solutions.

Embed with strategic higher-education partners to design, build, and ship production AI and platform integrations (LLM/RAG, LTI) across Cengage products. Own end-to-end deployments, write production code, ensure compliance and accessibility, evaluate AI safety and outcomes, mentor FDEs, and translate field learnings into product roadmap contributions.

Designs, trains, evaluates, deploys, and monitors scalable AI/ML models and data pipelines to meet federal customer requirements. Leads model architecture, testing, integration, and iterative improvement while ensuring data and model quality and presenting results to stakeholders.

Lead pre-sales and technical strategy for enterprise AI/ML engagements on Microsoft Data & AI. Drive solution design, architecture, executive presentations, and mentorship to close large deals and scale delivery playbooks aligned to security and governance.

Develop and optimize computer-vision and deep-learning models (segmentation, depth) and end-to-end ML pipelines for perception. Own lifecycle from prototype to production deployment on edge and cloud, evaluate performance on large datasets, and collaborate with cross-functional teams to integrate perception into autonomous tractors.

Develop, prototype, and transition machine learning, statistical, and graph-based analytics into production. Build models and synthetic data, extract features from diverse data formats, produce visualizations, validate performance using standard metrics, and lead analytic efforts to scale solutions while partnering with software and cloud engineers.

Design, build, deploy, and operate production-grade AI/ML and LLM systems (including RAG and agentic pipelines). Integrate AI capabilities with product workflows, run evaluation and monitoring, optimize model and system performance, and apply strong engineering practices (testing, CI/CD, versioning).

Build and scale production-ready generative AI applications: design multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deploy scalable FastAPI/Docker services with monitoring and optimization for latency, cost, and reliability.

Operate and monitor ML/AI models and agentic systems in production. Build AI observability, logging, tracing, and evaluation pipelines. Monitor LLM outputs, detect drift and model degradation, and maintain data/feature pipelines. Develop CI/CD, model versioning, experiment tracking, and automate alerts and incident remediation while collaborating with data scientists and platform teams.

Design, develop, and deploy machine learning and deep learning models; build end-to-end ML pipelines including preprocessing, training, evaluation, and deployment; perform feature engineering on large datasets; deploy and monitor models on cloud platforms; collaborate with cross-functional teams to integrate AI solutions and retrain models as needed.

Design, build, and deploy production-grade generative AI applications: orchestrate multi-agent systems, implement advanced RAG with vector DBs, fine-tune and host LLMs, optimize GenAI workflows, and deliver scalable FastAPI-based backend services with Docker and monitoring.

Design, build, and deploy production generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector databases, integrate/benchmark LLMs, fine-tune models, and deploy scalable FastAPI-based services with monitoring, CI/CD, and Dockerized production pipelines.

Design, build, and scale production Generative AI systems: multi-agent orchestration, advanced RAG with vector databases, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployment, monitoring, and GenAI optimization for latency, cost, and reliability.

Design, build, and deploy production-grade generative AI systems: multi-agent orchestration, advanced RAG with vector databases, integrate/swap LLMs, fine-tune models, optimize prompts and latency, and containerize services with monitoring and CI/CD.

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, deploy FastAPI async services in Docker, and optimize GenAI performance, cost, and monitoring.

Lead architecture, design, and delivery of Generative AI and ML solutions. Build scalable ML pipelines, LLM and RAG-based applications, vector DB integrations, and agent workflows. Drive MLOps, CI/CD, observability, security, and enterprise model orchestration. Provide technical leadership, mentor engineers, run Agile development, and shape AI strategy through POCs and platform innovation.

Design, build, and deploy production-ready generative AI applications: architect multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deliver scalable FastAPI-based services with monitoring, CI/CD, and containerized deployment.