Top Entry Level Artificial Intelligence Jobs

Develop and document compliant administrative cost allocation methodologies for federal and state workforce grants. Responsibilities include assessing current cost structures and controls, building allocation formulas and financial models, preparing public assistance cost allocation plans, supporting federal review, optimizing allowable reimbursement, and creating implementation, coding, reporting, and operational guidance. The role coordinates with fiscal and program leadership and supports financial reporting and federal claiming through December 2028.

Build backend services and AI systems for legal drafting, review, and search workflows. Model legal knowledge into ontologies, develop retrieval and evaluation infrastructure, test LLM prompts, optimize queries and performance, and deploy solutions with customers. The role also includes occasional frontend work in TypeScript and React, customer collaboration, and end-to-end ownership of product features.

Partners with business units to discover processes and challenges, identify and prioritize AI and GenAI opportunities, and translate them into use cases and requirements. Facilitates stakeholder workshops, maps current and future processes, defines user stories, KPIs, and value measures, and coordinates with data and AI engineering teams through UAT and adoption. Communicates business impact to executives and supports expanding client engagements.

Lead cross-functional AI data delivery projects from planning through implementation and adoption. Own project quality, timelines, economics, communications, risk resolution, and stakeholder transparency. Coordinate engineers, supply managers, clients, and other teams while managing project operations, status updates, retrospectives, and process improvements. The role involves client-facing delivery of data generation, evaluation, and red-teaming projects supporting large language model development and safety.

Leads federal opportunity capture strategy, pursuit coordination, competitive analysis, teaming, pricing, and win-probability improvement. Develops capture plans, evaluates customer needs and competitive landscapes, coordinates cross-functional teams, supports key-personnel recruiting, and presents progress to executives. Provides surge support for proposal management, content development, reviews, and sales-process improvement. Requires federal contracting knowledge, capture and proposal expertise, analytical ability, and familiarity with government contracting databases and tools.

The Content & Prompt Operations Specialist manages AI-assisted content workflows, ensuring accuracy and engagement in content produced. Responsibilities include writing prompts, drafting content, proofreading, and improving AI outputs while following structured processes.

Two-year leadership development program beginning in Level 1 customer support. Responsibilities include resolving client tickets, troubleshooting platform access, guiding dealership managers through data and reporting, and building customer-facing, product, sales, and operations expertise. After 12 months, participants advance to Associate Client Success Manager or Level 2 Implementation/Support Associate, followed by progression into Customer Success, Product Management, Marketing, or Sales.

Build and deploy generative AI applications and multi-agent systems on Google Cloud using Vertex AI, Python, BigQuery, RAG, and secure enterprise data integrations. Develop data pipelines, train and serve models, evaluate AI output, protect sensitive information, troubleshoot production systems, and collaborate with technical and business stakeholders.

Build and improve ML and data pipelines, ship full-stack product features, and enhance Mac and Windows desktop applications. Work directly with enterprise customers to clarify requirements, deploy solutions, and turn customer learnings into repeatable product improvements. The role requires high ownership, rapid execution, strong engineering practices, and measurable customer outcomes across applied ML, AI systems, product, and infrastructure.

Track and evaluate emerging LLM and AI research, assess models and techniques, build prototypes, and collaborate with engineers to deploy useful methods in client systems. Advise product direction, establish metrics for accuracy, reliability, and cost, and communicate research findings clearly. The role requires strong applied research, engineering, judgment, and communication skills in a fast-changing early-stage environment.