Top AI Engineer Jobs

Own and improve production AI dialogue systems, create a new automated assistant, establish evaluation, monitoring, and tracing practices, diagnose system failures, stay current with AI developments, and collaborate closely with operations and engineering teams.

Designs, develops, deploys, and supports large-scale AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, and model evaluation. Defines enterprise AI architecture, optimizes model performance, cost, latency, and throughput, establishes AI safety and governance standards, leads multi-year platform initiatives, and mentors senior technical leaders across engineering and research.

Design and implement AI agents for mission planning and orchestration across autonomous systems, focusing on LLM frameworks and edge computing.

Build and deploy production AI agents for real customers. Responsibilities include orchestrating LLM pipelines, integrating APIs and tools, developing React interfaces, implementing RAG and document-processing systems, deploying serverless applications, and monitoring performance. The role requires strong Python or JavaScript skills, hands-on OpenAI and multimodal LLM experience, vector databases, agent frameworks, function calling, and full-stack integration. Applicants must be pursuing a Master's or PhD in a technical field.

Lead the architecture and development of AI and NLP products that automate regulatory compliance for manufacturing companies. Own core models end to end, select technology stacks, guide experimentation and testing, balance speed with scalability, and collaborate with Product, Go-to-Market, Data, and customers to rapidly deliver production systems.

Design, develop, deploy, monitor, and maintain AI-enabled enterprise and cloud security solutions. Build machine learning workflows, data pipelines, and scalable security capabilities using Python, Java, TensorFlow, and related libraries. Analyze security risks and data quality, translate technical findings into client recommendations, support security controls across cloud and infrastructure environments, review deliverables, and mentor junior team members.

Leads the design and development of AI-driven enterprise and cloud security solutions. Manages teams delivering data, analytics, and machine learning engineering projects; oversees AI model implementation, data infrastructure, pipelines, and platform deployments. Uses Python and C++ for algorithm development, analyzes data, improves data quality, ensures technical compliance, mentors staff, manages client expectations, and drives innovation across cybersecurity and privacy initiatives.

Develop and improve AI models and agents powering an AI-native software testing platform. Responsibilities include supervised fine-tuning, creating training and evaluation datasets, building LLM evaluations and observability, tuning prompts and context, running experiments, and improving agent reliability, latency, token usage, and cost. The role also owns technical projects from definition through delivery and collaborates closely with engineering leadership.

Architects the data and AI foundation for marketing, sales, and analytics. Responsibilities include building Databricks pipelines, Gold Layer tables, semantic models, documentation, AI-enabled data products, and production LLM or agent systems. The role leads cross-functional initiatives, translates ambiguous business needs into scalable solutions, ensures data quality and reliability, mentors engineers, automates workflows, and partners with BI, data science, marketing, sales, and R&D teams.

Leads design, development, testing, deployment, and production support for secure Java microservices and REST or event-driven APIs. Owns DevOps execution, CI/CD pipelines, automated quality gates, resilience, observability, and comprehensive testing. Promotes responsible AI-assisted engineering practices, evaluates technology vendors, and guides teams on scalable AWS or private-cloud solutions using Cassandra and Kafka.

Build and run AI experiments supporting venture sourcing, research, investment decisions, and portfolio support. Develop software around messy data such as pitch decks, meeting notes, recordings, CRM history, and company data. Evaluate LLMs, agents, retrieval systems, and related AI applications while contributing with high autonomy in a small, collaborative team.

The Staff AI Engineer will analyze AI quality, implement improvements, manage projects, collaborate with teams, and shape AI capabilities in a remote-first environment.

Leads complex AI and cloud-native incubation projects, designing and implementing scalable services across public cloud, datacenter, and edge environments. Develops Generative AI, microservices, and distributed-system solutions; evaluates emerging technologies through prototypes and research spikes; guides architecture, security, reliability, testing, CI/CD, and observability. Leads small multidisciplinary engineering teams, collaborates with stakeholders, mentors engineers, and advances innovative capabilities across multiple parallel projects.

Lead the design, development, deployment, and governance of scalable AI systems, including foundation models, LLM inference, similarity search, guardrails, evaluation, and observability. Define AI platform strategy, optimize production systems, establish Responsible AI standards, and translate enterprise priorities into execution plans. Build and mentor multi-team engineering organizations while partnering with research, product, compliance, and risk teams.

Build, operate, and maintain scalable platforms supporting analytics and AI workloads. Responsibilities include cloud architecture across IaaS, PaaS, and SaaS; Terraform automation; Kafka and Kubernetes administration; platform monitoring, observability, cost optimization, reliability, and security controls. The role also provides platform support, documents operational practices, evaluates emerging technologies, and collaborates with architects, vendors, customers, and cross-functional teams.

Lead technical strategy and development of Confluence AI experiences across frontend, backend, and AI integrations. Design scalable systems, apply generative AI, retrieval, agents, evaluation, and safeguards, and improve product quality, adoption, trust, and customer value. Drive complex initiatives from architecture through launch, partner cross-functionally, mentor engineers, establish quality practices, and guide experimentation and continuous improvement across Atlassian.

Develop and productionize machine learning systems for search, retrieval, ranking, recommendations, conversational experiences, and AI agents. Own projects across the ML lifecycle, build evaluation datasets and benchmarks, conduct offline and online evaluations, analyze model quality, and improve system performance. Design scalable architectures addressing reliability, latency, privacy, and cost. Collaborate across product and engineering teams, communicate technical decisions, mentor engineers, and contribute to ML best practices.

Lead engineering and deploy scalable AI agents and agent orchestration at scale. Prototype GenAI solutions, design and operationalize distributed batch/real-time data flows, implement memory/RAG and vector DBs, integrate tool-calling/APIs, build calibration and guardrails, drive cloud adoption, and collaborate with global teams.

Lead the design, development, and deployment of autonomous AI agents and multi-agent systems. Build agentic workflows using planning, reasoning, memory, tool calling, RAG, vector databases, and orchestration frameworks. Integrate agents with enterprise systems and APIs while implementing evaluation, monitoring, observability, guardrails, and secure production practices. Provide technical leadership and collaborate across product, engineering, and business teams.

Fine-tune and align LLMs for specialized financial tasks; build agentic solutions with LangChain/Autogen; engineer ML pipelines for large financial datasets; deploy, monitor and scale production models; track AI research and integrate emerging tooling.

Build and operate AI agent applications that analyze enterprise code, identify and prioritize security risks, propose and verify fixes, and open merge-ready pull requests. Develop context engineering, evaluation benchmarks, and reliable production systems across thousands of repositories. Partner with customers on integrations, review code, and mentor engineers. Requires deep Python expertise, agentic workflow experience, application security knowledge, and production software development experience.

Leads the design and implementation of large-scale, cloud-native full-stack systems for cloud operations resilience. Develops distributed microservices using Python, Go, JavaScript, TypeScript, AWS, Docker, and Kubernetes. Defines platform roadmaps, architecture, engineering standards, and operational practices while improving reliability, performance, and technical debt. Mentors engineers, leads design reviews and cross-functional initiatives, evaluates emerging AI technologies, and influences enterprise technology strategy.

Build and lead development of cloud-based, full-stack systems supporting observability, connectivity, resilience, and availability. Design distributed microservices, define service boundaries, establish engineering standards, improve system performance, reduce technical debt, and implement AI-powered solutions. Collaborate with product managers and architects, influence technology strategy, mentor engineers, and drive cross-functional technical initiatives using Python, Go, JavaScript, TypeScript, AWS, Docker, and Kubernetes.

Design, develop, deploy, and optimize large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, orchestration pipelines, and observability. Lead cost-performance governance, technical design reviews, AI standards, roadmap development, and mentorship while improving model scalability, latency, throughput, hardware utilization, and inference costs.

Leads the architecture, development, deployment, and optimization of large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establishes enterprise AI architecture, performance and safety standards, and multi-year platform strategies. Partners across engineering, research, product, and program management while mentoring technical leaders and driving responsible AI adoption.