Top AI & Machine Learning Jobs

The AI Systems Engineer will build production systems for AI agents handling customer interactions, optimize omnichannel experiences, and integrate with CRM systems.

Perform data science, engineering, analytics, and statistical work for Department of War and Intelligence Community clients. Test and evaluate AI/ML models, collaborate with teams, communicate findings, and support or conduct all-source analysis. Experience with GEOINT and large language models is preferred. The role requires an active TS/SCI clearance and ability to pass a CI polygraph.

Own the architecture, roadmap, and production operation of Podium’s AI agent platform. Lead engineers building orchestration, tool use, memory, reasoning, evaluation, safety, and reliability systems for customer-facing agents. Drive capabilities from prototype to scale, optimize latency, cost, and quality, and partner with Product, Infrastructure, and customer teams. Establish engineering practices for testing and responsible deployment while representing AI Agents engineering in leadership decisions.

Supports enterprise AI transformation through machine learning, generative AI, data science, retrieval-augmented generation, and workflow automation. Responsibilities include data preparation, feature engineering, model development and evaluation, monitoring, dashboard and documentation creation, and secure AI application development. The intern collaborates with IT, AI transformation teams, data stewards, and business stakeholders to deliver practical AI and analytics solutions while following security, privacy, and responsible AI requirements.

Leads enterprise-wide AI strategy, governance, use-case development, vendor evaluation, pilot programs, implementation, change management, risk mitigation, and executive reporting. Partners with leaders across construction, manufacturing, service, VDC, estimating, business development, and finance to identify and scale high-value AI and automation initiatives while measuring ROI and adoption.

Build and own AI team systems spanning data ingestion, labeling, training and evaluation infrastructure, model deployment, perception integration, and customer-facing applications and APIs. Drive high-impact technical projects, make hands-on architectural decisions, mentor engineers, and support a broad AI software stack. The role is approximately 70% backend and 30% frontend, with focus areas including ML Ops, data pipelines, or computer vision.

Own evaluation systems and model-quality improvements for a generative content platform producing AI-generated ecommerce content. Build datasets, rubrics, automated judges, quality gates, and labeled training data from brand feedback. Quantify qualitative improvements, establish approval thresholds, and drive fine-tuning or retrieval experiments into production. The role requires strong software and system design skills, production service ownership, formal machine learning or statistics training, and experience measuring correctness in non-deterministic systems.

Develops production-ready AI, data automation, integration, and workflow solutions for engineering processes in an aerospace and defense environment. Responsibilities include programming, API and database integration, engineering toolchain integration, CI/CD, testing, documentation, workflow analysis, roadmap development, training, and user support. The role collaborates with systems engineers and customer teams and may deploy tools in classified environments.

Lead design and delivery of high-priority AI initiatives across product and platform boundaries. Rapidly prototype and launch AI features, unblock teams, mentor engineers, and shape production-grade LLM systems (retrieval, evals, prompting, orchestration). Collaborate with product, design, and domain experts to turn customer needs into shipped product.

Design, build, deploy, and optimize AI-powered agents with Salesforce Agentforce, LLMs, RAG, and agentic workflows. Integrate Salesforce with enterprise systems, configure Salesforce clouds and Einstein AI, and translate customer requirements into secure production solutions. Lead workshops, demonstrations, and technical discussions while contributing to DevOps, CI/CD, governance, documentation, and agile delivery. Serve as a client-facing technical partner connecting business needs with AI-enabled implementation.