Top AI Engineer Jobs

Build AI-powered workflows, agents, integrations, and internal tools that automate People Operations. Partner with People, Engineering, and IT to modernize onboarding and other employee processes, while implementing secure access controls, human oversight, escalation paths, and production-ready architectures. Prototype with users, iterate quickly, and measure improvements in efficiency, employee experience, and operational consistency.

Design, develop, deploy, and scale AI-powered solutions, digital workers, intelligent automation, and enterprise AI capabilities. Build Python-based generative and agentic AI solutions using LLMs, vector databases, embeddings, semantic search, and knowledge retrieval. Integrate AI with enterprise platforms and workflows, establish engineering and responsible AI standards, evaluate emerging technologies, and lead innovation pilots in collaboration with business, architecture, engineering, and operations teams.

Leads the design, development, and deployment of AI-powered solutions, intelligent automation, and digital workers. Prototypes and scales generative and agentic AI capabilities, integrates them with enterprise platforms and workflows, and establishes AI engineering, governance, security, privacy, and responsible AI standards. The role also leads technical workstreams, mentors engineers, evaluates emerging technologies, and drives enterprise adoption of innovative AI solutions.

Own architecture and direction for Suno’s AI Operations Engineering team. Build intelligent agents, workflows, internal tools, APIs, authentication patterns, and self-service deployment capabilities that help non-engineering teams scale. Evaluate third-party tools, integrate customer experience systems such as Zendesk, and partner with legal, finance, people, support, and content operations to identify and solve workflow challenges.

Build AI agents, workflows, internal tools, APIs, authentication patterns, and customer experience integrations that improve operations across support, legal, finance, people, and content teams. Evaluate build-versus-buy decisions, support self-service app deployment, and partner with stakeholders to identify automation opportunities. The role requires full-stack engineering experience and comfort working in an early-stage, rapidly evolving environment.

Design and develop real-time voice-to-voice AI agents using LLMs, speech recognition, speech synthesis, streaming, and agent frameworks. Build Python and C#/.NET backend services, APIs, telephony integrations, and RAG workflows. Optimize latency, reliability, and conversational performance while evaluating emerging AI technologies and collaborating with product and engineering teams.

Designs enterprise AI and agent architectures, including MCP servers, governed agent platforms, prompt harnesses, RAG and context-engineering systems. Authors architecture, security, SDLC, and governance artifacts; develops reusable AI accelerators; supports knowledge graph and ontology design; and creates enterprise architecture leadership materials. Acts as the technical liaison between enterprise architecture and service-line engineering teams, translating strategic decisions into practical implementation guidance.

Develop and maintain cloud software supporting cyber intelligence and AI/ML capabilities on AWS and large-scale compute clusters. Responsibilities include requirements analysis, Java development, cloud integration, distributed systems, RESTful services, testing, validation, troubleshooting, and sustainment. The role also involves Kubernetes, infrastructure as code, CI/CD, Linux scripting, Hadoop-based big-data technologies, microservices, LLMs, RAG, and security-conscious cloud engineering.

Design, optimize, and scale high-throughput, low-latency inference infrastructure for frontier LLM and generative AI models. Responsibilities include load scheduling, balancing, distributed systems, platform scalability, high availability, and rapid enablement of new AI capabilities. The role collaborates with internal and external partners to deliver production-grade Azure AI inference services.

Develop, test, deploy, and maintain artificial intelligence algorithms for insurance underwriting, pricing, and premium auditing. Write scalable Python code, apply commercial property and casualty insurance knowledge, integrate internal and third-party data, and collaborate with business and technical teams on research, design, debugging, and solution development. The hybrid role requires two weekly in-office days in Stamford, Connecticut.

Build and operate enterprise AIOps capabilities using Splunk, machine learning, ServiceNow integrations, and automated remediation. Manage Splunk architecture and clusters, develop advanced SPL and Python automation, aggregate telemetry across infrastructure tools, and support secure hybrid cloud observability. Lead technical working groups, guide infrastructure and development teams, and present designs and analyses to leadership. Support mission-critical DoD operations while applying RMF and ATO processes to AI/ML workloads.

Designs, deploys, and supports scalable AI/ML solutions, including predictive models, large language model applications, agentic workflows, and RAG pipelines. Builds production-grade MLOps and DevOps practices covering CI/CD, versioning, testing, monitoring, retraining, and rollback. Manages cloud deployments, observability, security, governance, HIPAA-aligned data practices, API integrations, documentation, and troubleshooting across data, infrastructure, model, and orchestration layers.

Provides technical leadership for enterprise AI security, designing secure AI platform architectures, vetting frameworks, identity and posture management for AI agents, and preventative security guardrails. The role requires building scalable security systems with Python, Go, or Java, managing infrastructure as code, addressing cloud and container security, mentoring engineers, and advising leadership on AI security risks.

Design, develop, and implement AI/ML software solutions and prototypes using full-stack technologies. Build REST APIs, integrate SQL and NoSQL databases, and collaborate with data science teams on AI/ML, big data, and business intelligence applications. Participate in Agile Scrum ceremonies, sprint delivery, testing, demonstrations, and retrospectives while solving complex technical problems in distributed teams.

Build pipelines that extract product knowledge from source code, APIs, file formats, and documentation into structured knowledge stores. Design ontologies, schemas, and knowledge graphs; develop parsers, typed interfaces, retrieval systems, and RAG layers; and decompose complex engineering workflows into callable operations. The role collaborates with domain engineers across multiple products and evaluates data quality, coverage, and schema completeness.

Designs, develops, and maintains backend services, REST APIs, data structures, and system architecture for conversational AI and automation applications. Integrates services with front-end systems, scheduling platforms, and EHRs. Implements authentication, authorization, HIPAA-compliant security controls, and secure PHI/PII handling. Optimizes performance, scalability, latency, reliability, and stability while collaborating with product, QA, integration, and engineering teams.

Leads the evaluation, design, development, and deployment of production AI/ML applications in AWS environments. Builds and optimizes machine learning models, assesses emerging technologies, presents technical recommendations, collaborates with federal clients, and mentors engineering teams. The role also supports cloud-native development, operations, security, enterprise AI adoption, and experimentation involving modern AI platforms and practices.

Design, develop, deploy, and operate distributed backend systems supporting large-scale, low-latency online model inference. The role owns inference platform technical direction, scalability, reliability, observability, and cloud infrastructure on GCP and Kubernetes. Responsibilities include partnering with ML engineers, optimizing high-throughput services, implementing DevOps and SRE practices, and monitoring production systems with Prometheus and Grafana.

Designs, develops, integrates, evaluates, and maintains AI and machine learning capabilities for NATO operational requirements. Responsibilities include generative AI, NLP, predictive analytics, model training, validation, deployment, monitoring, API and pipeline development, lifecycle management, responsible AI, and collaboration with technical and operational teams.

As a Founding AI Backend Engineer at Pakket, you'll enhance backend systems, debug the CV pipeline, and deploy services across warehouses, ensuring operational reliability and rapid feature deployment.

Own and deliver production-grade, AI-powered software features from design through deployment. Build generative AI user experiences, scale data ingestion and realtime querying across terabytes of data, optimize search, and train, deploy, integrate, and monitor machine learning models. Collaborate with engineers, deployment teams, and users while applying responsible AI practices and maintaining robust, well-tested code.

Own and deliver production-grade generative AI features that improve user experiences and operational intelligence. Scale systems handling terabytes of data, realtime querying and notifications, and high-performance search. Train, deploy, integrate, and monitor machine learning models while writing robust, tested software. Collaborate with product, engineering, deployment teams, and users to solve complex problems. This is an onsite role for candidates located in NYC.

Build and ship generative AI-powered software features for a large-scale public safety platform. Responsibilities include owning major application components, scaling terabytes of ingested data, enabling real-time querying and notifications, optimizing search, and training, deploying, integrating, and monitoring machine learning models. The role requires collaboration with product, engineering, deployment teams, and users, with a focus on responsible AI, reliable software, and production-grade development.

Build and maintain AI-integrated software, services, scripts, and internal tools. Design and evaluate prompts, validate AI-generated outputs, implement safeguards, debug applications and API integrations, write tested and documented code, participate in code reviews and system design, and monitor AI feature performance. Apply AI safety, hallucination detection, and output evaluation practices while collaborating through Git-based development workflows.

Build backend infrastructure for an AI revenue engine. Integrate large language models, optimize data pipelines, and develop scalable systems supporting autonomous revenue workflows for companies in the physical economy.