Top AI Engineer Jobs

Leads the design, development, optimization, and deployment of AI models and software applications. Develops APIs using Python and C#, manages complex AI projects, optimizes database queries, ensures data integrity, and collaborates with cross-functional teams. Responsibilities also include technical documentation, code reviews, bug fixes, mentoring junior engineers, and delivering AI solutions that meet business and performance requirements.

Build, integrate, test, deploy, and maintain AI-enabled applications, APIs, model services, automation workflows, and user-facing tools. Translate data science prototypes into production-ready software, implement evaluation and monitoring, optimize models and prompts, and collaborate with engineers and domain experts. The role requires Python, software development fundamentals, machine learning knowledge, Git, testing, and project experience, with preferred exposure to modern AI frameworks, cloud, containers, retrieval-augmented generation, and model evaluation.

Supports business process improvement by researching workflows, collecting data, maintaining project records, developing presentations and reports, and designing AI-powered automation solutions. The intern bridges business teams and technical systems using AI tools and automation platforms. This is an on-site summer internship in Austin, Texas, with no relocation or housing assistance.

The AI AppSec Engineer V will secure AI solutions, mentor teams, and ensure the integration of security into AI development lifecycles. Responsibilities include threat modeling, code analysis, and contributing to security policies.

Design and implement multi-step agentic AI workflows (orchestration, retrieval, tool use, state management) using frameworks like LangGraph/LangChain. Build retrieval pipelines over proprietary CRE data, evaluate and monitor model quality with human-in-the-loop processes, integrate Anthropic models on AWS Bedrock, implement guardrails and observability, and rapidly prototype features with Product and Design to deliver trustworthy, source-linked AI capabilities that perform real business actions.

The Principal Engineer will lead technical initiatives in AI-driven tax software, mentor engineers, enforce technical standards, and contribute to architectural design and product innovation.

Lead engineering for a real-to-sim platform supporting embodied AI and robot learning. Own the technical roadmap and requirements, prototype and ship critical features, benchmark policy transfer, diagnose sim-to-real failures, and evaluate robotics systems in real-world conditions. Collaborate with researchers, product leaders, customers, and test labs while advancing simulation, reinforcement learning, visual perception, and 3D scene representation capabilities.

Build and operate secure, scalable cloud-native services, user interfaces, backend services, and data pipelines for Relativity’s AI Platform. Responsibilities include writing maintainable tested code, participating in design and code reviews, supporting reliability and incident response, collaborating across engineering and product teams, and improving experimentation infrastructure for petabyte-scale legal data workloads.

Build production AI agent systems for healthcare workflows such as scheduling and medication refills. Develop orchestration, tool-use, conversation-state, recovery, evaluation, and ML pipelines. Analyze production conversations, identify failures, create evaluation datasets, measure agent quality, and improve reliability across models and prompts. Partner with research to productionize techniques while balancing quality, latency, and cost.

Researches, develops, tests, and maintains materials-aware machine learning models and software. Designs scalable ML systems, improves model interpretability and uncertainty quantification, and develops inverse-design capabilities. Collaborates with product, engineering, and external research teams to deliver customer-focused AI solutions, publishes research, reviews code, analyzes model performance, and mentors developers.

Design, build, deploy, and maintain AWS-based data and AI/ML infrastructure for enterprise agentic AI platforms. Develop data lake pipelines, observability systems, agent-enablement tooling, and production MLOps/LLMOps capabilities. Own features end-to-end, contribute to architecture and design decisions, optimize reliability, scalability, security, and cost, and collaborate with AWS Professional Services, client teams, and junior engineers.

Designs and builds secure, production-grade Generative AI and agentic AI applications using Python, LLMs, cloud services, and AI frameworks. Responsibilities include developing scalable AI pipelines, integrating Databricks data with AWS services, applying RAG and vector databases, evaluating models, implementing guardrails, researching emerging tools, improving application infrastructure, and guiding team members.

Leads the architecture, development, and deployment of production-grade agentic AI platforms. Builds multi-agent systems, RAG pipelines, fine-tuning applications, MCP servers, and full-stack applications using Python, Java, React, and microservices. Integrates vector and graph databases, develops prompt and intent logic, and collaborates with business subject matter experts to automate enterprise workflows. Provides technical leadership, mentors engineers, and ensures scalable, secure, production-quality AI solutions.

Leads a team of identity engineers responsible for a highly available PingFederate environment. Owns the SSO roadmap, zero-downtime upgrades, incident response, passwordless authentication modernization, OAuth/OIDC policies, and infrastructure-as-code deployments. Designs agentic AI workflows for anomaly detection and automated self-healing, while enforcing Terraform and GitOps standards across environments.

Design, train, deploy, monitor, and optimize speech and NLP models for a voice-agent platform. Build MLOps pipelines, APIs, and inference services while developing Android and iOS applications that integrate AI, voice, real-time communication, and backend services. Create frontend prototypes and dashboards, conduct model experiments, evaluate vendors, optimize performance, document systems, and maintain engineering quality across collaborative teams.

Design, build, deploy, and scale production AI applications and agents. Translate business problems into scalable AI solutions, develop backend services and APIs, integrate with enterprise data platforms, ensure observability, governance, and MLOps, and collaborate with cross-functional client teams.

Research, architect, and ship AI-powered product capabilities using agents, LLMs, RAG, embeddings, and machine learning. Build scalable multi-tenant AI infrastructure, agentic chat systems, data integrations, observability, evaluation frameworks, and guardrails. Translate business needs into measurable user-facing solutions while collaborating with product, data science, engineering, and leadership teams on the AI roadmap.

Project-focused AI engineering internship embedded on a DevOps team. The intern will prototype generative assistants for alert response, build retrieval-augmented systems over permissioned internal data, design guarded agentic remediation workflows, and support internal engineering Q&A. Responsibilities include tool-use workflows, observability integration, evaluation, documentation, Git-based delivery, and strict controls for permissioning, auditing, logging, safety, and cost.

Build and orchestrate AI automation workflows using n8n, LLMs, agentic frameworks, and MLflow. Develop intelligent agents, chatbots, semantic search systems, and AI dashboards using Python, Streamlit, OpenWebUI, vector databases, OpenAI, and Anthropic technologies. Integrate AI into business processes and data platforms while parsing documents and maintaining compliance, security, and ethical standards.

Leads the design, development, deployment, and governance of enterprise AI and machine learning systems. Builds production generative AI applications using LLMs, RAG, copilots, and automation; develops scalable pipelines, APIs, integrations, predictive models, and analytics. Partners with stakeholders to prioritize AI use cases, ensures model security and monitoring, evaluates emerging tools, drives rapid prototyping, documents solutions, and mentors engineers.

Designs, develops, fine-tunes, evaluates, and deploys machine learning and generative AI models, including LLMs, RAG systems, embeddings, and agents. Builds end-to-end ML pipelines, integrates Google Cloud data services, implements monitoring and drift detection, and applies responsible AI, security, governance, and compliance practices. Partners with stakeholders, data engineers, platform teams, DevOps/SRE, and junior engineers to deliver reliable AI solutions.

Design, develop, deploy, and optimize Generative AI applications using LLMs and AI frameworks. Build scalable APIs, automate workflows, integrate foundation models with enterprise systems, and improve model performance through testing and experimentation. Implement monitoring, quality assurance, governance, security, compliance, documentation, and risk mitigation. Evaluate emerging technologies, support proofs of concept, and collaborate with product, engineering, and business teams.

Leads the architecture, development, deployment, governance, and optimization of enterprise Generative AI solutions. Builds applications using LLMs and foundation models, develops prompt engineering frameworks, supports AI strategy, ensures secure and responsible deployment, mentors engineers, evaluates emerging technologies, and translates business needs into scalable technical architectures.

Build lightweight AI prototypes and workflow solutions for portfolio companies. Identify operational bottlenecks, develop proof-of-concepts using large language models and emerging AI technologies, document technical requirements for engineering handoff, and collaborate with investment, management, and engineering teams to drive AI adoption, operational efficiency, and business ROI.

Leads the design, development, governance, and enterprise adoption of secure Generative AI platforms and applications. Responsibilities include architecting RAG and agent-based solutions, developing APIs and reusable frameworks, establishing evaluation and prompt engineering standards, ensuring security and responsible AI practices, mentoring engineers, influencing architecture, and driving technical strategy across teams.