Ai Systems Engineer Jobs

About the Team The AI Deployment Engineering team is responsible for helping developers and enterprises safely and effectively deploy OpenAI technologies in production. We act as trusted technical advisors and thought partners for customers, working side by side with their teams to identify high-value use cases, design practical architectures, and move from prototype to durable deployment. Cybersecurity is one of the most urgent domains where AI can help. Security teams are under pressure to reason across code, logs, infrastructure, tickets, alerts, and vulnerability data faster than ever. As frontier models become more capable, organizations need deep technical guidance on how to evaluate, validate, and safely deploy AI systems in security-critical workflows. About the Role We are looking for a Cyber AI Deployment Engineer to partner with customers and help them apply OpenAI models, APIs, Codex, and agentic workflows to real cybersecurity use cases. You will work with CISOs, security executives, application security leaders, SOC teams, security engineering teams, and hands-on practitioners to identify where AI can create measurable security outcomes. This is a customer-facing technical role for someone who can move fluidly between executive strategy, practitioner-level cyber depth, and hands-on solution design. You will help customers evaluate and deploy workflows such as secure code review, vulnerability triage, threat modeling, remediation, SOC and incident response workflows, detection engineering, cloud security, GRC automation, and security validation. You will collaborate closely with Sales, Solutions Engineering, Product, Engineering, Research, and Security to turn customer needs into safe deployment patterns, reusable field assets, and product feedback. This role is based in our Singapore office. We offer relocation support to new employees. In this role, you will: Deeply embed with strategic customers as the technical lead for AI-enabled cybersecurity work

About the Team The AI Deployment Engineering team is responsible for helping developers and enterprises safely and effectively deploy OpenAI technologies in production. We act as trusted technical advisors and thought partners for customers, working side by side with their teams to identify high-value use cases, design practical architectures, and move from prototype to durable deployment. Cybersecurity is one of the most urgent domains where AI can help. Security teams are under pressure to reason across code, logs, infrastructure, tickets, alerts, and vulnerability data faster than ever. As frontier models become more capable, organizations need deep technical guidance on how to evaluate, validate, and safely deploy AI systems in security-critical workflows. About the Role We are looking for a Cyber AI Deployment Engineer to partner with customers and help them apply OpenAI models, APIs, Codex, and agentic workflows to real cybersecurity use cases. You will work with CISOs, security executives, application security leaders, SOC teams, security engineering teams, and hands-on practitioners to identify where AI can create measurable security outcomes. This is a customer-facing technical role for someone who can move fluidly between executive strategy, practitioner-level cyber depth, and hands-on solution design. You will help customers evaluate and deploy workflows such as secure code review, vulnerability triage, threat modeling, remediation, SOC and incident response workflows, detection engineering, cloud security, GRC automation, and security validation. You will collaborate closely with Sales, Solutions Engineering, Product, Engineering, Research, and Security to turn customer needs into safe deployment patterns, reusable field assets, and product feedback. This role is based in our Tokyo office. We offer relocation support to new employees. In this role, you will: Deeply embed with strategic customers as the technical lead for AI-enabled cybersecurity workflow

About the team The AI Deployment Engineering team is responsible for helping developers and enterprises safely and effectively deploy OpenAI technologies in production. We act as trusted technical advisors and thought partners for customers, working side by side with their teams to identify high-value use cases, design practical architectures, and move from prototype to durable deployment. Cybersecurity is one of the most urgent domains where AI can help. Security teams are under pressure to reason across code, logs, infrastructure, tickets, alerts, and vulnerability data faster than ever. As frontier models become more capable, organizations need deep technical guidance on how to evaluate, validate, and safely deploy AI systems in security-critical workflows. About the role We are looking for a Cyber AI Deployment Engineer to partner with customers and help them apply OpenAI models, APIs, Codex, and agentic workflows to real cybersecurity use cases. You will work with CISOs, security executives, application security leaders, SOC teams, security engineering teams, and hands-on practitioners to identify where AI can create measurable security outcomes. This is a customer-facing technical role for someone who can move fluidly between executive strategy, practitioner-level cyber depth, and hands-on solution design. You will help customers evaluate and deploy workflows such as secure code review, vulnerability triage, threat modeling, remediation, SOC and incident response workflows, detection engineering, cloud security, GRC automation, and security validation. You will collaborate closely with Sales, Solutions Engineering, Product, Engineering, Research, and Security to turn customer needs into safe deployment patterns, reusable field assets, and product feedback. This role is based in our San Francisco HQ. We offer relocation support to new employees. In this role, you will: Deeply embed with strategic customers as the technical lead for AI-enabled cybersecurity work

About the Team OpenAI’s Applied AI Engineering team helps organizations turn frontier AI capabilities into safe, reliable, and high-impact production systems. We work with customer executives, product and engineering teams, security leaders, and transformation teams to identify valuable opportunities, accelerate technical implementation, and scale what works. Enterprise deployments are defined by complexity rather than any one industry: existing architectures, diverse data environments, security and governance requirements, multiple stakeholder groups, and organization-wide change. We turn lessons from these deployments into better products and reusable patterns for customers everywhere. About the Role As an Enterprise Applied AI Engineer, you will partner directly with leading organizations to design, build, and deploy AI systems that deliver measurable business outcomes. You will combine deep technical judgment, hands-on engineering, and customer leadership to take ambitious ideas from use-case selection and architecture through prototyping, evaluation, production launch, and scale. You will write and debug code, build evaluation systems, resolve complex integrations, and guide decisions involving model behavior, reliability, latency, cost, safety, security, governance, and operational readiness. Success is measured by production systems, sustained adoption, and meaningful customer impact—not simply activity or successful demonstrations. This is a rare opportunity to work on consequential real-world deployments at the frontier of AI while directly influencing how OpenAI’s products evolve. In this role, you will: Partner directly with enterprise customers to identify high-value opportunities and translate them into technical architectures, implementation plans, evaluation strategies, and measurable success criteria. Design, build, and deploy AI systems that solve important customer problems and produce measurable business outcomes. Work hands-on in code to build pr

About the Team OpenAI’s Applied AI Engineering team helps organizations turn frontier AI capabilities into safe, reliable, and high-impact production systems. We work with customer executives, product and engineering teams, security leaders, and transformation teams to identify valuable opportunities, accelerate technical implementation, and scale what works. Enterprise deployments are defined by complexity rather than any one industry: existing architectures, diverse data environments, security and governance requirements, multiple stakeholder groups, and organization-wide change. We turn lessons from these deployments into better products and reusable patterns for customers everywhere. About the Role As an Applied AI Engineer you will partner directly with leading organizations to design, build, and deploy AI systems that deliver measurable business outcomes. You will combine deep technical judgment, hands-on engineering, and customer leadership to take ambitious ideas from use-case selection and architecture through prototyping, evaluation, production launch, and scale. You will write and debug code, build evaluation systems, resolve complex integrations, and guide decisions involving model behavior, reliability, latency, cost, safety, security, governance, and operational readiness. Success is measured by production systems, sustained adoption, and meaningful customer impact—not simply activity or successful demonstrations. This is a rare opportunity to work on consequential real-world deployments at the frontier of AI while directly influencing how OpenAI’s products evolve. This role can be based in Delhi, Mumbai or Bangalore. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees In this role, you will: Partner directly with enterprise customers to identify high-value opportunities and translate them into technical architectures, implementation plans, evaluation strategies, and measurable success criteria.

About the Team OpenAI’s Applied AI Engineering team helps organizations turn frontier AI capabilities into safe, reliable, and high-impact production systems. We work with customer executives, product and engineering teams, security leaders, and transformation teams to identify valuable opportunities, accelerate technical implementation, and scale what works. Enterprise deployments are defined by complexity rather than any one industry: existing architectures, diverse data environments, security and governance requirements, multiple stakeholder groups, and organization-wide change. We turn lessons from these deployments into better products and reusable patterns for customers everywhere. About the Role As an Applied AI Engineer you will partner directly with leading organizations to design, build, and deploy AI systems that deliver measurable business outcomes. You will combine deep technical judgment, hands-on engineering, and customer leadership to take ambitious ideas from use-case selection and architecture through prototyping, evaluation, production launch, and scale. You will write and debug code, build evaluation systems, resolve complex integrations, and guide decisions involving model behavior, reliability, latency, cost, safety, security, governance, and operational readiness. Success is measured by production systems, sustained adoption, and meaningful customer impact—not simply activity or successful demonstrations. This is a rare opportunity to work on consequential real-world deployments at the frontier of AI while directly influencing how OpenAI’s products evolve. This role is based in Sydney, Australia. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees In this role, you will: Partner directly with enterprise customers to identify high-value opportunities and translate them into technical architectures, implementation plans, evaluation strategies, and measurable success criteria. Design, build

About the Team OpenAI’s Applied AI Engineering team helps organizations turn frontier AI capabilities into safe, reliable, and high-impact production systems. We work with customer executives, product and engineering teams, security leaders, and transformation teams to identify valuable opportunities, accelerate technical implementation, and scale what works. Enterprise deployments are defined by complexity rather than any one industry: existing architectures, diverse data environments, security and governance requirements, multiple stakeholder groups, and organization-wide change. We turn lessons from these deployments into better products and reusable patterns for customers everywhere. About the Role As an Applied AI Engineer you will partner directly with leading organizations to design, build, and deploy AI systems that deliver measurable business outcomes. You will combine deep technical judgment, hands-on engineering, and customer leadership to take ambitious ideas from use-case selection and architecture through prototyping, evaluation, production launch, and scale. You will write and debug code, build evaluation systems, resolve complex integrations, and guide decisions involving model behavior, reliability, latency, cost, safety, security, governance, and operational readiness. Success is measured by production systems, sustained adoption, and meaningful customer impact—not simply activity or successful demonstrations. This is a rare opportunity to work on consequential real-world deployments at the frontier of AI while directly influencing how OpenAI’s products evolve. This role is based in Seoul, South Korea. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees In this role, you will: Partner directly with enterprise customers to identify high-value opportunities and translate them into technical architectures, implementation plans, evaluation strategies, and measurable success criteria. Design, buil

About the team The AI Deployment Engineering team works closely with frontier startups. We are trusted advisors to, and thought partners with, startups to ensure that OpenAI’s technology is deployed safely and effectively, whilst also partnering with engineering, research, and product to turn those insights into evaluation systems, product improvements, and better model behavior. This team sits at the intersection of customer reality and model quality. We combine hands-on technical depth with strong product judgment, helping translate complex, high-value use cases into clear signals that can improve both the customer experience and the underlying systems. This role is based in Paris. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. We require fluency in French for this role. About the role We are seeking a technically proficient, product-minded engineer to help push the frontier of advanced AI with our strategic startup customers. You'll work with some of the most exciting AI startups in the world, helping them optimize their own systems and turning those learnings into durable improvements across OpenAI’s research and products. You will partner deeply on complex workflows, identify the gaps that matter, and help transform those gaps into reproducible evaluations, technical insights - helping shape OpenAI's research and product direction. This role is well suited to engineers who are equally comfortable debugging a workflow, iterating on prompts or agents, designing evaluations, and collaborating across research and product. You should be excited by ambiguous, high-impact problems and motivated by the opportunity to shape how advanced AI systems improve in practice. In this role, you will: Work directly with strategic startup customers to understand critical workflows, uncover failure modes, and identify high-impact opportunities for improvement. Prototype and iterate on prompts, agents, and workflow designs to better unde

About the team The AI Deployment Engineering team works closely with frontier startups. We are trusted advisors to, and thought partners with, startups to ensure that OpenAI’s technology is deployed safely and effectively, whilst also partnering with engineering, research, and product to turn those insights into evaluation systems, product improvements, and better model behavior. This team sits at the intersection of customer reality and model quality. We combine hands-on technical depth with strong product judgment, helping translate complex, high-value use cases into clear signals that can improve both the customer experience and the underlying systems. About the role We are seeking a technically proficient, product-minded engineer to help push the frontier of advanced AI with our strategic startup customers. You'll work with some of the most exciting AI startups in the world, helping them optimize their own systems and turning those learnings into durable improvements across OpenAI’s research and products. You will partner deeply on complex workflows, identify the gaps that matter, and help transform those gaps into reproducible evaluations, technical insights - helping shape OpenAI's research and product direction. This role is well suited to engineers who are equally comfortable debugging a workflow, iterating on prompts or agents, designing evaluations, and collaborating across research and product. You should be excited by ambiguous, high-impact problems and motivated by the opportunity to shape how advanced AI systems improve in practice. This role is based in London. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. In this role, you will: Work directly with strategic startup customers to understand critical workflows, uncover failure modes, and identify high-impact opportunities for improvement. Prototype and iterate on prompts, agents, and workflow designs to better understand system behavior and unlock customer

Who We Are Notion is the collaborative AI workspace where teams and agents think together . We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion. Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work. About the Role: Millions of people use Notion — and this number is increasing every day. That means millions of people trust us to deliver a fast, reliable, and secure experience, and we value this more than anything. We want to keep earning trust, while also continuing to amaze our users with the tools they can build in Notion. The AI Platform team is responsible for building the shared foundations that let Notion ship AI products quickly and operate them safely at scale. You’ll join a team of talented engineers focused on making speed and quality compatible: reliability and availability through provider changes, quality and correctness systems like evals and release gates, observability that makes failures explainable, and shared primitives for model integrations, context management, long-running actions, and cost/performance tradeoffs. Notion’s AI platform is vital to helping product teams move faster with production-grade guardrails as models, providers, and AI capabilities rapidly evolve. This role can be based in either San Francisco or New York City. We work from our offices on Mondays, Tuesdays and Thursdays (our Anchor Days)

About Meesho Meesho is India's fastest-growing internet commerce company, on a mission to democratize e-commerce for everyone. We serve millions of customers and over 1.75 million sellers through technology-driven innovation, building the scalable systems that power Meesho's most critical surfaces — Search, Recommendations, Personalized Ranking, Logistics, Fraud Detection, and Image Match. The AI Platform sits at the heart of this. It serves a peak of 1M+ real-time deep-learning model inferences per second on ordinary days, scaling 3x+ on sale days — with the reliability that scale demands. The team works at the frontier of applied AI and infrastructure — multi-region inference, novel embedding-search algorithms, and optimized open-weight LLM models — squeezing out every bit of computation and passing the cost savings straight back to customers. About the Role We are looking for an experienced Engineering Manager – AI Engineering to lead the development of scalable AI platforms and infrastructure while managing high-performing engineering teams. You will drive the design, delivery, and optimization of production-grade AI systems powering AI use cases across Meesho.

NK Securities Research is a leading financial firm that leverages cutting-edge technology and sophisticated algorithms to trade the financial markets. Founded in 2011, we have gained invaluable experience in the field of High-Frequency Trading (HFT) across different asset classes. Role Overview We’re looking for engineers who can take AI work beyond experiments and make it hold up in production. You’ll work closely with quant researchers and infra engineers to build AI systems that actually get used improving research speed and internal tooling without slowing down the core stack. We value engineers who think about trade-offs, test what they build, and care about how things run in production. What You’ll Build Production AI Ship models that meet defined latency and reliability expectation Add monitoring, rollback, and guardrails before anything goes live Optimise inference across CPU/GPU environments when it matters Integration into Real Systems Plug AI into data-heavy workflows without hurting performance Work within existing low-latency architecture instead of fighting it Profile and remove bottlenecks rather than guessing AI for Engineers & Researchers Build tools that genuinely speed up research and development Improve code understanding, review workflows, and internal knowledge retrieval Keep systems auditable and predictable LLM & Retrieval Systems Implement structured RAG and embedding pipelines with validation in place Create safe integration layers between models and internal systems Performance & Standards Track latency, drift, and stability — not just accuracy Build observability into everything you ship Help raise the bar for how AI is engineered here What We’re Looking For Strong Python fundamentals Clear thinking around system design and performance trade-offs Experience deploying AI systems in production (1–5 years is typical) Familiarity with transformers, embeddings, or LLM deployment Nice to have: Exposure to C++ / Rust / Go E

Who We Are HP IQ is HP’s new AI innovation lab. Combining startup agility with HP’s global scale, we’re building intelligent technologies that redefine how the world works, creates, and collaborates. We’re assembling a diverse, world-class team—engineers, designers, researchers, and product minds—focused on creating an intelligent ecosystem across HP’s portfolio. Together, we’re developing intuitive, adaptive solutions that spark creativity, boost productivity, and make collaboration seamless. We create breakthrough solutions that make complex tasks feel effortless, teamwork more natural, and ideas more impactful—always with a human-centric mindset. By embedding AI advancements into every HP product and service, we’re expanding what’s possible for individuals, organisations, and the future of work. Join us as we reinvent work, so people everywhere can do their best work. About The Role The AI team is building cutting-edge solutions that bring the power of AI directly to edge devices while seamlessly integrating with cloud infrastructure. We are looking for a Lead Software Engineer to design and develop high-performance, scalable services to support AI workloads across edge and cloud environments. What You Might Do Design, build, and maintain services that power AI-driven applications, ensuring scalability and performance. Develop APIs and microservices that facilitate seamless integration between cloud-based AI models and edge devices. Optimize data pipelines and storage solutions for real-time AI inference and processing. Implement security and privacy best practices for distributed AI systems. Work closely with AI researchers, infrastructure engineers, and frontend developers to deliver end-to-end AI-driven solutions. Build and optimize an agent orchestration runtime that enables tool use, memory management, and multi-step reasoning across LLMs, APIs, and edge-connected systems. Develop robust logging, monitoring, and alerting systems to ensure system reliability.

Who We Are HP IQ is HP’s new AI innovation lab. Combining startup agility with HP’s global scale, we’re building intelligent technologies that redefine how the world works, creates, and collaborates. We’re assembling a diverse, world-class team—engineers, designers, researchers, and product minds—focused on creating an intelligent ecosystem across HP’s portfolio. Together, we’re developing intuitive, adaptive solutions that spark creativity, boost productivity, and make collaboration seamless. We create breakthrough solutions that make complex tasks feel effortless, teamwork more natural, and ideas more impactful—always with a human-centric mindset. By embedding AI advancements into every HP product and service, we’re expanding what’s possible for individuals, organisations, and the future of work. Join us as we reinvent work, so people everywhere can do their best work. About The Role The AI team is building cutting-edge solutions that bring the power of AI directly to edge devices while seamlessly integrating with cloud infrastructure. We are looking for a Senior Software Engineer to design and develop high-performance, scalable services to support AI workloads across edge and cloud environments. What You Might Do Design, build, and maintain services that power AI-driven applications, ensuring scalability and performance. Develop APIs and microservices that facilitate seamless integration between cloud-based AI models and edge devices. Optimize data pipelines and storage solutions for real-time AI inference and processing. Implement security and privacy best practices for distributed AI systems. Work closely with AI researchers, infrastructure engineers, and frontend developers to deliver end-to-end AI-driven solutions. Build and optimize an agent orchestration runtime that enables tool use, memory management, and multi-step reasoning across LLMs, APIs, and edge-connected systems. Develop robust logging, monitoring, and alerting systems to ensure system reliabilit

Role: Senior AI Engineer Location: Hyderabad, India (Hybrid) Department: Product Development About the Role GHX is building a cutting-edge LLM-powered document understanding platform focused on classification, structured data extraction, and intelligent orchestration at scale. This is a high-impact AI engineering role where you will own the full lifecycle—from problem framing to production deployment . Initially, you will focus on prompt engineering and evaluation systems , building the quality foundation for AI performance. Over time, the role expands into agent orchestration, system architecture, and migration of rule-based systems to LLM-driven pipelines . A strong foundation in software engineering (5+ years) is essential. This role demands engineering rigor across both traditional system design and AI system behavior . Core Responsibilities 1. Prompt Engineering Design prompts for diverse document classification and extraction tasks Treat prompts as formal specifications (precise, structured, and edge-case-aware) Develop few-shot, chain-of-thought, and structured output templates Manage prompt lifecycle: versioning, testing, and rollback 2. LLM Output Evaluation Create and maintain ground truth datasets Build automated evaluation pipelines (precision, recall, field-level accuracy) Identify and resolve conceptually incorrect outputs despite surface correctness 3. AI Agent Orchestration Design multi-agent workflows for document processing Implement tool-use patterns and integrate MCP servers Optimize orchestration for scale and efficiency 4. Software Engineering Develop production-grade APIs and backend services Apply Clean Architecture / DDD principles Write maintainable, testable Python code Contribute to CI/CD, deployment, and observability systems 5. Stakeholder Collaboration Act as a bridge between business stakeholders and AI systems Translate product requirements into technical architectures Communicate system behavior, limitations, and quality