Jobs at Nexxa.ai

Manage technical project execution across Applied AI and Core Engineering teams. Own project decomposition, estimation, timelines, dependencies, risk tracking, and status reporting. Coordinate engineering, DevOps, testing, and client delivery teams; identify blockers and support technical triage across AI agents, infrastructure automation, workflow orchestration, CI/CD, and platform architecture. Drive prioritization, communication, iterative delivery, and execution against shared business goals.

Own product design across Nexxa’s AI platform, creating polished UX/UI for complex industrial workflows, dashboards, command centers, AI agents, recommendations, approvals, and automation. Develop interaction patterns, prototypes, motion, transitions, and responsive experiences while simplifying dense information. Collaborate with product, engineering, AI, and leadership, contribute to the design system, and take concepts through production. Strong visual taste, systems thinking, UX expertise, and comfort with ambiguity are essential.

Own and evolve cloud, on-premises, and edge infrastructure supporting AI, data, and product workloads. Design Kubernetes platforms with GPU scheduling, CI/CD pipelines, infrastructure-as-code, observability, security, and reliability practices. Lead infrastructure projects, incident response, roadmap strategy, and platform development while balancing cost, latency, resilience, and developer velocity. Mentor engineers and support machine learning training, inference, data pipelines, and industrial system integrations.

Own and evolve cloud, on-premises, and edge infrastructure supporting AI, data, and product workloads. Design Kubernetes platforms, GPU scheduling, CI/CD pipelines, infrastructure-as-code, observability, security, and reliability practices. Lead infrastructure projects, incident response, platform strategy, and developer enablement while balancing cost, latency, reliability, and velocity. Mentor engineers and provide technical direction for scalable, production-grade ML and industrial systems.

Own Nexxa’s SOC 2 and ISO 27001 compliance programs while securing multi-account AWS and GCP environments. Responsibilities include cloud and infrastructure remediation, IAM administration, identity lifecycle management, endpoint and SaaS operations, auditor and customer reviews, CI/CD and software supply-chain hardening, production troubleshooting, and security documentation. The role builds automated controls, manages findings, supports internal IT, and establishes scalable security and AI governance processes.

Design, train, evaluate, and deploy production computer vision and vision-language systems for industrial applications. Build pipelines for detection, segmentation, OCR, tracking, multimodal reasoning, and real-time edge or cloud inference. Develop data and evaluation workflows, fine-tune foundation models, and optimize deployments through quantization, pruning, and hardware acceleration. Collaborate across AI, engineering, research, and product teams to deliver scalable, robust visual AI solutions.

Own SOC 2 Type 2 and ISO 27001 compliance programs while securing AWS, GCP, SaaS, infrastructure, identity, CI/CD, and software supply chains. Triage and remediate security findings, administer access and endpoints, support IT operations, manage auditors and customer reviews, debug cloud production issues, and build automated security guardrails, documentation, and postmortems.

Design and operate production backend services and AI infrastructure, including model-serving pipelines, inference services, data pipelines, RAG systems, vector stores, APIs, and microservices. Build scalable real-time and batch systems, integrate LLM, computer vision, and machine learning models, and manage deployment, observability, reliability, and performance. Collaborate with ML, product, and field engineering teams, document architectures, and mentor engineers.

Design, train, evaluate, and deploy production computer vision systems for industrial applications. Build detection, segmentation, classification, OCR, tracking, and visual-language pipelines; optimize real-time inference for edge and cloud environments; develop data and evaluation pipelines; fine-tune multimodal foundation models; and improve performance through quantization, pruning, and hardware acceleration. Collaborate across AI, engineering, product, and research teams on architecture, technical design, and production-grade AI systems.

Design and operate production backend services and AI infrastructure, including model serving, inference, data pipelines, vector stores, RAG systems, APIs, and microservices. Architect scalable real-time and batch systems, integrate and deploy LLM, computer vision, and machine learning models, and ensure reliability, observability, testing, and CI/CD. Collaborate with ML, product, and customer-facing teams while documenting systems and mentoring engineers.

The role involves delivering technical solutions, integrating AI/ML models, developing software, and mentoring junior engineers while ensuring customer satisfaction through engagement and solution design.

As a Senior Forward Deployed Engineer, you'll deliver AI solutions, engage with customers, and lead project lifecycles, including deployment and integration.

The Senior Forward Deployed Engineer will deliver technical solutions using AI, engage with customers, and lead project lifecycles while mentoring junior engineers.

Own the commercial funnel from outreach to contract signing, building strategic B2B engagements in complex industrial settings, driving executive conversations and managing multi-stakeholder relationships.

The Product Manager will define the product roadmap, prioritize features, collaborate with engineering on architecture, and track KPIs for an AI platform in heavy industries.

The Solutions Architect designs system architectures and project plans based on customer requirements, coordinates with engineering teams, and owns the technical aspects of engagements.