Top AI Engineer Jobs

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.

Design and implement scalable data solutions using Microsoft Fabric and Azure. Build and orchestrate ETL/ELT pipelines, develop Power BI reports and DAX, optimize T-SQL and KQL queries, model data for Lakehouse/warehouse architectures, integrate varied data sources with Spark/PySpark, automate processes, ensure governance and security, and troubleshoot pipeline issues.

Build AI-powered automation and data tools for aerospace engineering teams. Responsibilities include processing telemetry and engineering datasets, developing agentic AI systems and MCP servers, integrating automation into CI/CD and mission workflows, creating dashboards, evaluating AI outputs, and taking prototypes into production. The role requires end-to-end ownership, user collaboration, testing, monitoring, and measurable efficiency improvements in flight-critical environments.

Build AI-powered automation and data tools for aerospace engineering teams. Responsibilities include processing telemetry and engineering datasets, developing agentic AI systems and MCP servers, integrating tools into CI/CD and mission workflows, creating dashboards, evaluating AI outputs, and taking prototypes into production. The role involves partnering with engineers across disciplines, measuring efficiency gains, and maintaining flight-critical software.

Architect and lead production AI products, including chatbots, document processing systems, and agentic workflows. Build FloQast’s centralized AI platform with model routing, vector search, provider management, observability, and MCP integrations. Integrate AI systems with accounting platforms, repositories, APIs, and cloud environments. Establish AI engineering standards, ensure compliance and reliability, and mentor engineers while providing technical leadership across the organization.

Take Deepgram's speech models and optimize, compile, and run them on resource-constrained embedded and edge hardware. Work across model compression, runtime performance (C/C++/Rust), RTOS/bare-metal and embedded Linux environments, vendor NPUs/DSPs and inference runtimes, benchmarking, deployment pipelines, OTA updates, and partner with silicon vendors to enable real-time, low-power on-device voice experiences.

Leads enterprise Model Context Protocol platform strategy, architecture, security, governance, and implementation across hybrid and multi-cloud environments. Designs MCP gateways and control planes connecting AI agents, APIs, applications, and data. Establishes authentication, authorization, observability, Zero Trust controls, deployment patterns, CI/CD, infrastructure-as-code, governance, and lifecycle standards. Partners with architecture, cybersecurity, cloud, and engineering teams to enable secure, scalable, responsible AI adoption.

Develops enterprise software and AI-enabled product features using C#, .NET, Azure, LLMs, prompt engineering, retrieval-based systems, and agentic workflows. Responsibilities include designing APIs and cloud components, testing, debugging, optimizing applications, supporting CI/CD and production monitoring, and collaborating with product, architecture, QA, DevOps, security, and tax subject matter teams. The role emphasizes secure, scalable, maintainable SaaS solutions and continuous improvement in an Agile environment.

Build and maintain Python-based services that integrate Generative AI into enterprise data and ML platforms. Design, implement, and scale cloud-hosted solutions, mentor junior developers, ensure governance and security compliance, and evaluate emerging AI technologies to improve developer productivity and business value.

Develop and maintain production AI/ML applications using agent-based workflows, LLM integration, RAG pipelines, REST APIs, and Python services. Design agent architectures, process structured and unstructured data, refine PostgreSQL schemas, and optimize applications for performance, reliability, scalability, and usability. The role also supports full-stack development, enterprise deployment, tool integration, memory management, vector search, and cloud or containerized environments.

Participate in the Gradient Development Program, complete technical training, pursue Google Cloud certifications, and collaborate with technical and business leads to translate business requirements into effective AI and machine learning solutions.

Build full-stack software, backend services, APIs, frontend experiences, and shared integration capabilities supporting AI initiatives across Keeper’s products. Integrate LLMs, agents, and tool-calling workflows into secure production environments while maintaining zero-knowledge principles. Troubleshoot cross-product systems, improve reliability and observability, contribute to architecture and code reviews, automate testing and CI/CD, and collaborate with AI, product, and engineering teams.

Build and deploy cloud-native generative AI and agentic AI solutions for federal missions. Support model selection, agent development, tool calling, response synthesis, CI/CD, infrastructure automation, container orchestration, production monitoring, performance optimization, documentation, and cross-functional collaboration with engineering, data, and stakeholder teams.

Lead the design, development, and deployment of scalable Generative AI applications using Python. Build integrations with GenAI APIs, implement RAG systems, develop backend services, write automated tests, optimize performance and security, and collaborate across product, data science, and engineering teams. The role also provides technical guidance to developers, contributes to architecture and coding standards, and supports risk management and regulatory compliance.

Design and implement evaluations for AI agents across voice, vision, and browser use. Build benchmarks, simulations, and scoring systems for customer-specific scenarios, then use evaluation findings to improve agent behavior, context, and underlying models.

Architect and build high-performance distributed infrastructure for batch and streaming data across global regions. Optimize state management, checkpointing, JVM performance, and exactly-once processing. Develop AI infrastructure including vector indexing, agentic workflows, and real-time data streaming. Own the full software development lifecycle, contribute to shared tooling and SDKs, and mentor engineers through technical leadership and design reviews.

Develop, deploy, and support full-stack applications using React, JavaScript or TypeScript, C#, .NET, relational databases, REST APIs, and Azure. Partner with business users to modernize prototypes into secure, scalable production applications. Apply enterprise architecture, authentication, authorization, security, performance, and vulnerability-management practices. Build integrations, troubleshoot production issues, and collaborate with infrastructure, security, data, and architecture teams. Use AI-assisted development tools such as Claude Code and Codex extensively.