Top AI Engineer Jobs

Leads the design and delivery of secure, scalable distributed systems and AI-enabled applications. Responsibilities include architectural leadership, Java or Scala development, cloud engineering, CI/CD, infrastructure automation, API integration, retrieval-augmented generation, agentic workflows, responsible AI adoption, mentoring, and establishing engineering quality and security standards.

Design and build cloud-agnostic Databricks Lakehouse architectures, scalable ETL/ELT pipelines, and production AI data workflows. Optimize Apache Spark performance, integrate MLflow for model deployment, and implement CI/CD and Infrastructure-as-Code using Terraform or Databricks CLI. Enforce governance, security, and compliance standards through Unity Catalog and cloud platforms. Collaborate with architects, scientists, product managers, and client engineering teams to deliver documented, production-ready solutions.

Full-stack software engineering intern partnering with senior engineers to build and maintain applications across front-end, back-end, APIs, databases, and cloud infrastructure. Responsibilities include feature development, system design, automated testing, code reviews, CI/CD, infrastructure as code, production monitoring, incident response, and cross-functional collaboration. The role also involves using and critically reviewing AI-generated code and integrating AI tools into software development.

Full-stack Software Development Engineer Intern building end-to-end features across front-end, back-end, APIs, databases, cloud infrastructure, and AI-integrated components. Responsibilities include system design, coding, testing, code review, CI/CD, infrastructure as code, production monitoring, incident response, and collaboration with product managers, designers, and engineers. Interns will use AI coding assistants and LLM APIs while evaluating generated code for correctness, security, performance, and maintainability.

Develops data pipelines, entity and topic extraction, embeddings-based search, AI model fine-tuning, inference optimization, and APIs for enterprise legal and risk applications. Builds evaluation platforms and metrics to improve model, search, and algorithm quality. Requires expertise in LLMs, machine learning frameworks, programming, cloud-native development, scalable data pipelines, advanced search, CI/CD, and technical leadership.

Leads architecture and hands-on delivery of production-grade software, machine learning, generative AI, RAG, and AI-agent solutions. Builds cloud-native services, APIs, data integrations, MLOps/LLMOps pipelines, evaluation frameworks, and secure deployment patterns. Owns technical initiatives from experimentation through production, including reliability, observability, testing, incident resolution, responsible AI, and cost optimization. Influences cross-team architecture and roadmaps while mentoring engineers without direct management responsibilities.

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.

Build and operate the backend for Bilt’s AI-powered Concierge across chat, SMS, email, and iMessage. Responsibilities include agent tools and workflows, multi-agent orchestration, model routing, distributed systems, messaging, authentication, performance optimization, evaluation harnesses, and proactive engagement features. The role requires production backend experience, LLM familiarity, cloud service operation, strong distributed-systems fundamentals, product judgment, AI coding-agent usage, and clear technical writing.

Build and deploy production generative AI solutions using Microsoft 365 Copilot, Copilot Studio, and Azure AI Foundry. Responsibilities include developing agents, RAG pipelines, integrations, evaluation frameworks, monitoring, security controls, and CI/CD practices. The role involves working directly with client stakeholders to scope solutions, demonstrate progress, train users, and support adoption. Candidates need software or data engineering experience, hands-on LLM development, Azure expertise, and strong Python or C# skills.

Leads global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

Unpaid AI engineering internship to design, build, and deploy ML solutions (NLP, computer vision, recommenders). Work on data preprocessing, model training/fine-tuning, containerized FastAPI microservices, and scalable deployments on Google Cloud Run. Contribute to code reviews, GitHub repos, and integration of third-party AI APIs.

Develop end-to-end data and machine learning capabilities for Special Operations users. Responsibilities include building scalable pipelines, transforming large datasets, applying advanced analytics and machine learning, deploying models and inference services, developing APIs, and delivering analyst-facing applications. The role requires collaboration with mission users, analysts, engineers, and technical leadership in a customer-site environment.

Build and operate Arm’s production AI platform infrastructure, including model gateways, MCP services, and control-plane components. Own scalability, reliability, observability, security, incident response, automation, lifecycle management, and operational readiness. Develop infrastructure-as-code and GitOps workflows, secure runtime patterns, telemetry, monitoring, alerting, and support processes across Kubernetes, cloud, identity, networking, secrets, and compliance systems. Participate in a paid on-call rotation for high-impact incidents.

Provides end-to-end application and infrastructure support for production systems, ensuring stability, availability, and performance. Responsibilities include monitoring environments, troubleshooting incidents, supporting ITIL incident/problem/change management, analyzing operational trends, and escalating issues. The role also promotes validated AI-assisted engineering practices, automation, secure coding, testing, and remediation. Required technical experience includes Python, Terraform, Kubernetes, microservices, public cloud, observability tools, and large-scale technology environments.

Build Kyte’s applied AI platform, including digital twins, autonomous agents, memory systems, detection frameworks, evaluation tools, and distributed infrastructure supporting thousands of agents. Work closely with the CTO, make scalable architectural decisions, engage directly with customers, and own projects end to end. The role requires full-stack development, large-scale data processing, rapid product delivery, and communication of complex technical concepts to nontechnical audiences.

Leads full-stack software engineering for JPMorganChase’s Wholesale Credit Risk technology team. Designs, develops, tests, troubleshoots, and reviews secure production code; improves application resilience and operational stability; promotes responsible AI-assisted engineering practices; automates recurring remediation; and evaluates architectures and technologies with vendors and internal teams. The role requires cloud-native development, agile SDLC expertise, financial services knowledge, data architecture, modern front-end technologies, GraphQL, and API development.

Design, develop, deploy, and support scalable full-stack applications and cloud-native platform services with embedded AI capabilities. Build custom AI agents, RAG systems, LLM integrations, MCP-based tools, and intelligent automation workflows. Own solutions across architecture, implementation, testing, observability, security, deployment, and lifecycle support, with emphasis on reliability for critical manufacturing systems. Collaborate with engineering, product, and business stakeholders while applying strong software engineering and AI-assisted development practices.

Lead design and deployment of physics-based, ML, and LLM-powered modeling and agentic AI systems for semiconductor process, packaging, and manufacturing workflows; architect integrations with design automation and fab/test platforms; benchmark and improve models; and translate results into actionable insights and visualizations.

Own the full lifecycle of AI products, from discovery and roadmap definition through prototyping, deployment, adoption, and measurable business outcomes. Build GenAI and agentic AI solutions using LLM APIs, tool calling, structured outputs, and evaluation. Partner with data engineering on pipelines, data quality, governance, security, and observability. Lead agile backlog management, UX collaboration, training, change management, and continuous improvement while translating ambiguous business needs into production AI applications.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead architecture, cost-performance governance, AI standards, and design reviews while mentoring senior technical staff. Partner with research, engineering, product, and program teams to establish foundational AI roadmaps and deliver scalable, responsible systems.

Designs, develops, deploys, and supports large-scale AI systems, including foundation-model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. The role optimizes AI infrastructure for scalability, latency, throughput, cost, and hardware utilization; defines reliability objectives; leads architecture and technical reviews; and mentors engineering staff.

Design, develop, deploy, and optimize foundational AI systems, including large language model inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture, cost-performance optimization, technical standards, and design reviews for scalable production AI systems. Mentor senior technical staff and contribute to the long-term roadmap for responsible AI infrastructure.

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Optimizes large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Leads architecture and design reviews, establishes AI engineering standards, drives technical roadmaps, and mentors senior AI engineers.

Design, develop, optimize, deploy, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead optimization of production AI systems for scalability, latency, throughput, hardware utilization, and cost. Architect multi-model orchestration pipelines, establish governance standards, lead design reviews, contribute to technical roadmaps, and mentor senior AI engineering staff.

Design, develop, deploy, and support foundational AI systems, including LLM training and inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, reliability, and hardware utilization. Own complex system architecture, define AI service-level objectives, lead technical reviews, ensure security and compliance, contribute to long-term roadmaps, and mentor engineering staff.