AI Engineer jobs in New York, NY

Design, build, and optimize high-performance C# systems, backend services, and tooling for AI data pipelines, annotation, validation, and evaluation workflows. Improve production system reliability, performance, and safety, and collaborate with research, data, and engineering teams to resolve bottlenecks and support model training and evaluation.

Design, build, and optimize high-performance C++ systems, backend services, and tooling for AI data pipelines, annotation, validation, quality control, and evaluation. Improve existing systems' reliability, performance, and correctness, identify and resolve bottlenecks, and collaborate with research, data, and engineering teams on architecture and implementation.

Lead the design, development, and optimization of Python backend systems, data pipelines, annotation tools, APIs, and evaluation infrastructure for AI model training and assessment. Improve production system reliability, performance, and safety, and guide architecture decisions through design and code reviews with cross-functional teams.

Define and implement agentic AI architectures and policy-as-code standards to optimize cloud financial management across multi-cloud environments. Lead delivery squads in executing FinOps automations, forecasting, and executive reporting to drive measurable business value.

Design and develop intuitive graphical user interfaces for AI agents, including conversational and multimodal interactions. Build interactive dashboards and client-facing applications that integrate with backend services, LLMs, and enterprise systems.

You will lead the technical direction and operational strategy for Nscale's front-end AI infrastructure networks, ensuring high performance and scalability. This includes designing large-scale Ethernet fabrics, acting as a senior escalation point for complex incidents, and mentoring engineering teams.

You will build and demonstrate connected AI solutions by integrating products, models, and agents to deliver measurable customer outcomes. This role involves creating working prototypes, API integrations, and compelling business stories to communicate technical value to executives and customers.

Design, build, and operate the backend services and infrastructure powering Within’s AI platform, including data ingestion, APIs, background processing, and reliable AI orchestration workflows. Set technical direction for critical systems, establish security and evaluation practices, and partner across product and engineering while mentoring teammates.

Own the operational health, configuration consistency, and performance of large-scale Ethernet front-end networks supporting AI inference, management, and storage workloads. Lead complex incident response and postmortems, improve automation and monitoring with SRE and engineering teams, and tune network performance and reliability.

The AI Platform Engineering Lead drives the strategy, architecture, and governance for AI/ML infrastructure, including GPU clusters, RAG pipelines, and agentic AI systems. They manage the AI Platform Operations team, oversee budgets, and ensure scalable, secure, and compliant platform operations across the enterprise.

Develop and test full stack, cloud-native applications on AWS, translating client needs into solutions across architecture, design, implementation, and testing. Troubleshoot application and platform issues, apply AI-enabled engineering methods, collaborate with engineering teams, and review work while mentoring junior colleagues.

Provide senior technical leadership for Analytics Engineering, shaping data foundations, modeling standards, architecture, governance, quality, tooling, and developer experience. Build trusted canonical datasets, semantic layers, and AI-enabled analytical tools that support self-service analytics and improve decision-making across the company.

You will design, implement, and deploy scalable AI solutions while partnering with clients across various industries to address complex business challenges. Additionally, you will provide thought leadership in scientific communities and guide non-technical teams in software development best practices.

The Data Engineer will build and maintain low-level data pipelines using Databricks and Apache Spark while coordinating with onshore and offshore teams. They are also responsible for supporting deployment, post-production maintenance, and communicating project status to engagement managers.

Drive technical wins for complex Cloud AI workloads by supporting the business cycle from evaluation through customer ramp. Provide deep technical consultation and develop prototypes to secure buy-in from customer domain experts.

Design, prototype, develop, and deploy domain-specific AI agents that collaborate with other agents to gather information, generate insights, and perform intelligent tasks. Partner with business, transformation, and AI platform teams to deliver scalable solutions, monitor and optimize production systems, and validate their reliability and performance.

Own and evolve CVector's backend platform, including time-series data systems, AI-assisted analytics, cloud infrastructure, and data ingestion pipelines. Lead complex systems and technical migrations while shaping platform reliability, scalability, and the integration of AI into industrial energy workflows.

Instrument and capture engineering and AI telemetry, partner on production data pipelines, and define trustworthy metrics for AI investment and developer experience. Build executive-facing dashboards and analyses, communicate recommendations to senior stakeholders, and identify opportunities to improve AI ROI and developer productivity.

You will own the technical strategy and architecture for go-to-market systems while designing and operating AI-powered agents to automate sales workflows. You will lead technical execution across teams, partnering with business leaders to deliver scalable solutions that improve sales productivity.

Collaborate with stakeholders to analyze business requirements and translate them into structured, executable instructions for AI agents. Design, implement, and maintain reusable Claude Code Skills while evaluating their performance through iterative testing.

Collaborate with stakeholders to analyze business requirements and translate them into structured, executable instructions for AI agents. Design, implement, and maintain reusable Claude Code Skills while evaluating their performance through iterative testing.

The role involves designing and securing AI-enabled products and cloud environments using Zero Trust principles and secure software development lifecycles. You will collaborate with stakeholders to implement production-quality AI security architectures and automated threat management capabilities.

The engineer will design and implement secure architectures for generative and agentic AI, applying Zero Trust controls across cloud environments and development pipelines. They will also lead client discovery sessions and develop production-quality AI security platforms while mentoring team members.

Own and evolve backend, time-series data, and AI infrastructure, including database migrations, industrial data ingestion, API architecture, and distributed modeling workflows. Improve system reliability, observability, deployment practices, and developer experience while collaborating across product, modeling, and frontend teams.

Own the end-to-end design and execution of AI-driven platform initiatives and integration infrastructure. Lead junior and mid-level engineers to improve team productivity through systems design and technical direction.