Senior Solution Architect – Sales AI Applications

🔥 0 minutes ago

🇮🇳 India – Remote

⏰ Full Time

🟠 Senior

💸 Sales Engineer

👻 Ghost score 11%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Collaborate with application teams to design, develop, and maintain scalable full-stack solutions for enterprise sales workflows • Guide technical solutions across front-end, back-end, APIs, data services, integrations, and cloud infrastructure • Translate product requirements and business needs into secure, maintainable solutions and intuitive user experiences • Integrate generative AI models, AI services, APIs, retrieval systems, and agentic workflows into production applications • Design application architectures supporting performance, availability, observability, security, scalability, and long-term maintainability • Lead technical design discussions, compare implementation approaches, make architecture decisions, and evaluate emerging technologies • Improve engineering practices for testing, code quality, continuous integration and delivery, monitoring, documentation, and production readiness • Investigate complex issues and develop solutions improving reliability and user experience • Mentor engineers, share technical knowledge, and contribute to engineering standards and collaborative team practices

🎯 Requirements

• A bachelor’s degree or equivalent experience in Computer Science, Engineering, or a related technical field is encouraged • 10+ years of professional software engineering experience, including building and operating production applications • Experience developing full-stack applications with modern front-end, back-end, and web application technologies • Proficiency in one or more languages or frameworks, such as Python, Java, JavaScript, React, Node.js, or similar technologies • Experience designing APIs, distributed applications, data services, enterprise integrations, scalable cloud applications, databases, and messaging systems • Experience integrating AI or machine learning capabilities through APIs, models, retrieval systems, or AI services • Knowledge of software architecture, system design, security, testing, observability, and production operations • Ability to lead technical initiatives, make informed engineering decisions, communicate with technical and non-technical teams, and mentor engineers • Experience building generative AI applications, including AI assistants, retrieval-augmented generation, agentic workflows, AI productivity tools, Python-based AI services, large language model APIs, vector databases, prompt engineering, or AI evaluation • Experience combining traditional software systems with AI models and data pipelines and progressing AI capabilities from prototype through production • Familiarity with responsible AI, data privacy, access controls, security, or enterprise AI governance • Experience with Kubernetes, containers, microservices, infrastructure as code, DevOps practices, application performance, reliability, scalability, observability, cloud cost efficiency, or globally distributed engineering teams