Join FlexAI: 

FlexAI is at the forefront of revolutionizing AI computing by reengineering infrastructure at the system level. Our groundbreaking architecture, combined with sophisticated software intelligence, abstraction, and an orchestration layer, allows developers to leverage a diverse array of compute, resulting in efficient, more reliable computing at a fraction of the cost. We are seeking a skilled and experienced Enterprise AI & Cloud Solutions Engineer

Founded by Brijesh Tripathi and Dali Kilani, who bring experience from Nvidia, Apple, Tesla, Intel, Lifen, and Zoox, FlexAI is not just building a product – we’re shaping the future of AI. Our teams are strategically distributed across Paris, Silicon Valley, and Bangalore, united by a shared mission: to deliver more compute with less complexity.

If you're passionate about shaping the future of artificial intelligence, driving innovation, and contributing to a sustainable and inclusive AI ecosystem, FlexAI is the place for you !
 

Position Overview:

The Enterprise AI & Cloud Solutions Engineer will serve as a technical expert in AI workload deployment, DevOps, and infrastructure management across on-prem and multi-cloud environments. The role focuses on fine-tuning, inferencing, and training AI models efficiently across hybrid infrastructures. This person will work closely with customers, ensuring seamless deployment, troubleshooting, and optimization of high-performance AI workloads.

What you'll do:

AI Workload Deployment & Infrastructure Management:

- Lead the deployment, integration, and optimization of AI workloads (training, fine-tuning, inferencing) across cloud and on-prem environments
- Work directly with customers to set up high-performance computing (HPC) clusters, configure GPUs/TPUs, and optimize AI workloads
- Automate deployment pipelines for AI models using Kubernetes, Terraform, and CI/CD workflows across AWS, GCP, Azure, and on-prem clusters
- Implement best practices for scaling AI workloads dynamically across hybrid and multi-cloud environments

DevOps & Performance Optimization:

- Optimize GPU and accelerator utilization to maximize AI training efficiency
- Troubleshoot model performance by profiling workloads, identifying bottlenecks, and optimizing resource allocationWork with the engineering team to build self-healing, auto-scaling AI infrastructure

Customer Success & Technical Support:

-Act as the primary technical liaison for enterprise customers, ensuring smooth onboarding and ongoing success
-Conduct technical deep dives with customer DevOps and engineering teams to optimize cloud/on-prem AI workloads
- Train customers onfine-tuning best practices, distributed training methodologies, and inference deployment strategies

Cloud & On-Prem AI Orchestration:

- Guide customers in choosing the best deployment model (on-prem, hybrid, multi-cloud) based on workload and cost-efficiency
- Deploy, maintain, and monitor AI model lifecycle management solutions to improve operational efficiency
- Work with cloud providers (AWS, Azure, GCP) to optimize cost-performance trade-offs for AI workloads

What you'll need to be successful: 

Technical Skills (DevOps & AI Infrastructure)

- AI Workload Optimization - Strong understanding of fine-tuning, training, inferencing for LLMs, vision models, and HPC workloads
- Cloud & On-Prem Deployment - Deep expertise in deploying AI workloads on AWS, Azure, GCP, and hybrid on-prem clusters
- DevOps & Infrastructure as Code - Experience with Kubernetes, Docker, Terraform, Ansible, Helm for AI model deployment
- GPU & Accelerator Optimization - Familiarity with NVIDIA A100, H100, MI300X, TPU, Habana, Tenstorrent optimizations
- MLOps & AI Lifecycle - Experience with MLflow, Weights & Biases, Kubeflow, Airflow, Hugging Face for AI pipeline management
- Networking & Storage for AI - Proficiency in InfiniBand, NVMe, distributed training techniques (Horovod, DeepSpeed, FSDP)

Customer-Facing & Problem-Solving Skills

- Ability to translate complex AI infrastructure topics into clear explanations for enterprise clients
- Experience in managing customer expectations, debugging real-time AI performance issues, and guiding technical roadmaps
- Strong communication and collaboration skills, especially in cross-functional teams involving AI engineers, DevOps, and customer success teams

Bonus Experience (Preferred)

- Certifications - AWS Certified DevOps Engineer, Google Cloud ML Engineer, Kubernetes Administrator (CKA)
- Experience with AI Training Frameworks - TensorFlow, PyTorch, JAX, Triton Inference Server
- Previous Experience in CSP or AI-focused Cloud Services

What we offer:

• A competitive salary and benefits package, tailored to recognize your dedication and contributions.
• The opportunity to collaborate with leading experts in AI and cloud computing, learning from the best and the brightest, fostering continuous growth.
• An environment that values innovation, collaboration, and mutual respect.
• Support for personal and professional development, empowering you with the tools and resources to elevate your skills and leave a lasting impact.
• A pivotal role in the AI revolution, shaping the technologies that power the innovations of tomorrow.
  

Offices :

Our teams are strategically distributed across three continents—Europe, North America, and Asia—united by a shared mission: to deliver more compute with less complexity.

• Paris - HQ
• San Francisco (Bay Area) - US office
• Bangalore - India office

Apply NOW!

You’ve seen what this role entails. Now we want to hear from you! Does this opportunity align with your aspirations? If you’re even slightly curious, we encourage you to apply – it could be the start of something extraordinary!

At FlexAI, we believe diverse teams are the most innovative teams. We’re committed to creating an inclusive environment where everyone feels valued, and we proudly offer equal opportunities regardless of gender, sexual orientation, origin, disabilities, veteran status, or any other facets of your identity that make you uniquely you.