Top AI Engineer Jobs

Designs and deploys production AI solutions and data platforms, leads architecture and implementation discussions, presents technical solutions to customers and executives, evaluates AI trade-offs, and identifies revenue-growth opportunities within existing accounts.

Build customer-facing AI prototypes, API integrations, agent workflows, and demonstrations across Salesforce AI Labs products and enterprise systems. Identify high-value customer use cases, communicate technical and business value to diverse audiences, experiment with agentic architectures, measure outcomes, and create reusable reference architectures, integrations, environments, and playbooks. Feed technical findings and customer needs into product decisions.

Build complete applications, workflow automations, and basic AI agents using AI coding tools. Translate customer needs into specifications, deploy solutions, measure usage and outcomes, evaluate AI-generated code for security and reliability, identify automation opportunities through customer conversations, and document effective prompts and development patterns.

Serves as a hands-on technical leader for emerging technology and AI experimentation. Designs prototypes, proofs of concept, pilots, integrations, and architecture evaluations; writes and reviews prototype code; troubleshoots complex technical issues; and develops reusable standards and recommendations. Collaborates with architecture, security, data, risk, legal, procurement, vendors, and delivery teams to transition validated concepts into scalable solutions. Mentors engineers and communicates findings, tradeoffs, risks, and adoption recommendations to technical and business stakeholders.

Designs and maintains automated risk-reporting solutions, integrating Python, Shell, SQL, AI workflows, Power BI, and dimensional data models. Modernizes SAS-based processes into scalable Python and Anaconda pipelines, develops data marts and star schemas, tunes queries, and supports risk-system production operations, governance, controls, monitoring, and change management. Partners with reporting teams and stakeholders to improve data accuracy, reliability, visualization, and operational effectiveness.

Develop, evaluate, deploy, and maintain AI systems for engineering and business applications. Responsibilities include building model and agent evaluations, datasets, training environments, automated graders, data workflows, internal integrations, and secure infrastructure. The role uses Docker and Kubernetes for reliable workloads, supports experiment tracking and debugging, and requires collaboration with engineering, security, legal, HR, and business partners to ensure auditability, data protection, reliability, and measurable performance improvements.

Build and own internal AI systems from prototype through production, including agents, workflow integrations, document assistants, and CRM tools. Translate workflow problems into technical requirements, develop reliable applications, implement evaluations and observability, design security and human review controls, deploy services, and improve systems using failure data. Collaborate directly with nontechnical colleagues and document solutions for maintainability.

Build and operate secure, governed AI infrastructure and platform capabilities across AWS, model access, identity, observability, automation, agent execution, and enterprise integrations. Develop infrastructure as code, CI/CD automation, telemetry and cost-management pipelines, sandboxed agent environments, mediation services, monitoring, audit evidence, onboarding templates, runbooks, and operational support models. Partner with architects and AI teams to deliver reusable infrastructure for client-facing and internal AI solutions.

Designs, fine-tunes, evaluates, and deploys LLM-based applications using RAG, prompt engineering, autonomous agents, and NLP techniques. Builds scalable AI systems and training pipelines across cloud and local environments, integrates AI into enterprise tools and products, leads architecture decisions, applies emerging research, and mentors team members. Requires strong Python and deep learning expertise, advanced technical education, and at least two years of relevant experience for master’s degree holders.

Design, build, deploy, and maintain resilient, scalable backend services and platform components for Toast's Voice AI platform. Lead cross-cutting architecture and projects, integrate AI platforms (LLMs), implement observability and evaluation for AI systems, set engineering standards, and mentor engineers.

Develop full-stack tools and automation for deploying and managing cloud infrastructure. Responsibilities include designing software solutions, collaborating with business partners, developing architecture and automation strategies, reporting on projects, and providing technical leadership. The role requires React and Go development, API integration, CI/CD, automated testing, version control, security-focused coding, troubleshooting, documentation, and applying AI technologies to improve workflows and decision-making.

Design and operate production software supporting scientific data and wet-lab workflows. Own compound registration, experimental data ingestion, validation, transformation, publishing, APIs, data pipelines, and integrations with scientific platforms. Partner with scientists to translate ambiguous research operations into scalable technical solutions. Establish architecture, testing, monitoring, CI/CD, deployment, documentation, and operational practices for reliable cloud-native systems.

Build and deploy LLM-powered agents for enterprise clients, including scoping workflows, writing Microsoft 365 and business-system connectors, configuring secure tenant deployments, measuring adoption and outcomes, training users, and handing solutions into maintenance. The role requires client-facing collaboration, production-grade coding, governance documentation, and delivery within fixed-fee engagements. Engineers may use TypeScript, Python, Microsoft Graph, SharePoint, Teams, Outlook, MCP, Claude, ChatGPT, or Copilot.

Designs and delivers customer-facing AI solutions from discovery and proof of concept through deployment. Responsibilities include solution architecture, LLM/RAG/agent implementation, API and data pipeline integration, model deployment and monitoring, troubleshooting, technical training, and post-deployment support. The role evaluates business impact, coordinates cross-functional delivery, and helps establish implementation processes for scalable customer deployments.

Architect and deploy enterprise LLMs, autonomous multi-agent systems, and real-time AI solutions for supply chain operations. Build secure cloud infrastructure, event-driven pipelines, evaluation frameworks, data integrations, and proactive automation. Lead architectural decisions, establish LLMOps and MLOps standards, mentor engineers, and collaborate with business leaders to deliver production AI products.

Provides technical leadership and hands-on engineering for Epic’s AI-powered developer platforms, including code review systems, cloud agent hosting, knowledge bases, and developer tools. Establishes AI frameworks, agent architectures, security and governance standards, and scalable service patterns across CLI, desktop, web, build integrations, APIs, and microservices. Mentors engineers, leads architecture decisions, defines success metrics, and owns zero-to-one platform development and operations at scale.

Provides technical leadership and architectural direction for production-grade AI developer tools and platforms used across Epic. Designs scalable CLI, desktop, web, API, and service-based systems; establishes AI agent and framework standards; implements security, governance, identity, secret management, and runtime isolation; defines success metrics; partners with leadership; and mentors engineers while remaining hands-on with development, testing, deployment, and operations.

Designs and operates production-quality AI framework, runtime, benchmarking, performance, automation, and observability components. Benchmarks, profiles, debugs, and optimizes large language model training and inference across GPUs and Microsoft hardware. The role drives projects from definition through deployment, investigates cross-stack reliability issues, partners with research, infrastructure, model, and hardware teams, contributes to technical standards, and mentors engineers.

Leads the architecture and development of AI-driven frontend systems, LLM integrations, agentic interfaces, intelligent bots, and scalable enterprise applications. Provides technical leadership for Agile teams, establishes coding standards, mentors engineers, translates AI capabilities into business value, defines requirements and roadmaps, and supports complex integrations and customer escalations. The role requires modern frontend expertise with React and TypeScript alongside Java, Spring Boot, REST services, cloud, databases, and AI developer tooling.

Own the production lifecycle of AI capabilities, including deployment, serving, release automation, monitoring, rollback, orchestration, and backend integration. Design reliable online and batch inference systems while partnering with ML, data, product, and engineering teams. Lead MLOps and distributed-systems initiatives, improve observability and operational resilience, support incident response, mentor engineers, and ensure secure, privacy-conscious handling of healthcare data.

Build AI-enabled applications, automations, integrations, data workflows, and prototypes across Salesforce, MuleSoft, .NET, RPA, and enterprise platforms. Partner with business and IT stakeholders to identify opportunities, validate measurable value, assess AI and architectural risks, and deliver supportable solutions. Document designs, dependencies, prompts, limitations, runbooks, and handoffs while contributing to exploratory engagements and recommending pivots when initiatives lack value.

Build scalable data infrastructure for LLMs and autonomous AI agents. Responsibilities include automated fine-tuning pipelines, real-time telemetry and evaluation, synthetic data generation, data quality and curation, MLOps integration, and vector database management for RAG workflows. The role requires strong Python and SQL skills, experience with LLM APIs, open-source models, orchestration tools, cloud infrastructure, distributed processing, and synthetic data techniques.

Own production inference for large language models across single and multi-node GPU systems. Select and tune precision, quantization, parallelism, memory strategies, batching, concurrency, topology, and serving runtimes. Benchmark and validate latency, throughput, utilization, and cost; operate model lifecycles and resolve incidents across runtimes, GPUs, hosts, drivers, and networks. Build software that automates deployment decisions and captures inference expertise for reliable multi-tenant and dedicated serving.

Develop C#/.NET APIs for an enterprise data platform, perform gap analysis, and build REST integrations using microservices and event-driven architecture. Implement CI/CD, containerization, and API management. In a later phase, integrate LLMs, build RAG and AI APIs, use orchestration frameworks, tune prompts and model parameters, and implement AI gateway capabilities such as token limits and model routing.

Lead a hands-on platform engineering function spanning full-stack product development, real-time Kafka and ClickHouse data/ML pipelines, cloud infrastructure, DevOps, security, and reliability. Own React, Node.js, and MongoDB applications; Terraform, Kubernetes, AWS/GCP, Docker, and GitHub Actions; production observability and incident response; and SOC 2 readiness. Establish AI-assisted engineering workflows using tools such as Claude Code and Cursor while replacing vendor-built infrastructure with durable internal automation.