At Navan, we aren't building a single, generic chatbot. We are building a Composable AI Microservice Architecture, a swarm of hundreds of hyper-specialized AI services, each meticulously "programmed" to solve small, focused tasks with high precision. This fleet powers Ava, our AI support engine, and a suite of cutting-edge generative tools for travel and expense management.
As a Senior AI Operations (AI Ops) Engineer, you are the architect of the platform that makes this scale possible. You will move beyond traditional MLOps to manage a "factory" of Language Models. Your challenge is one of orchestration and standardization, ensuring that every service in the swarm meets a rigorous bar for quality, reliability, and cost-efficiency.
What You’ll Do- Orchestrate the AI Fleet: Build and own the runtime environment for 100+ specialized AI services. Manage model routing, context versioning, and standardized memory/history stores.
- High-Density Inference Optimization: Design and implement SageMaker Multi-Model Endpoints (MME) and Inference Components to serve multiple tuned SLMs per GPU, maximizing hardware utilization while minimizing latency.
- Deterministic Service Excellence: Treat reliability as a layered engineering problem. Build deterministic "shells" around probabilistic LM outputs, prioritizing data-layer validation and strict serialization.
- Automated Evaluation & Observability: Implement "LLM-as-a-judge" patterns and automated benchmarking to detect semantic drift and hallucinations across the fleet before they impact the user.
- Standardize the Workflow: Obsess over building reusable patterns and Terraform-based infrastructure that eliminate "snowflake" configurations, allowing us to deploy new specialized AI tasks in minutes.
- Agency Strategy: Partner with AI Researchers to find the "Goldilocks zone" for agentic autonomy—balancing the flexibility of LLM tool-use with the precision required for production stability.
- Experience: 5+ years in SRE, Platform Engineering, or MLOps, with at least 2 years focused on deploying LLMs/SLMs in production environments.
- SageMaker Mastery: Deep hands-on expertise with AWS SageMaker, specifically configuring Multi-Model Endpoints (MME), Inference Components, and GPU-backed instances (G5/P4).
- SLM Expertise: Proven experience with Small Language Models (e.g., Mistral, Llama 3, Phi) and parameter-efficient fine-tuning (PEFT) deployment strategies like LoRA/QLoRA.
- Technical Stack: * Languages: Strong proficiency in Python and Terraform.
- Orchestration: Experience with Docker, Kubernetes (EKS), or AWS ECS/Fargate.
- Data: Familiarity with Snowflake and Vector Databases.
- The "AI Ops" Mindset: You understand that AI at scale is a statistical challenge. You are comfortable debugging issues at the data/serialization layer rather than defaulting to prompt tweaks.
- CI/CD & Automation: Experience building robust pipelines (Jenkins, GitHub Actions) for non-deterministic software, including automated "eval" stages.
- Education: BS or MS in Computer Science, Engineering, Mathematics, or a related technical field.
Must have
- Python, Terraform, Sagemaker
Top Skills
What the Team is Saying
What We Do
Navan (Nasdaq: NAVN) is the leading all-in-one business travel, payments, and expense management platform that makes travel easy for frequent travelers. From finding flights and hotels to automating expense reconciliation, with 24/7 support along the way, Navan delivers an intuitive experience travelers love and finance teams rely on. See how Navan customers benefit and learn more at navan.com.
Why Work With Us
At Navan, we’re never satisfied with the status quo, and we know breakthrough ideas come from diverse perspectives. We are committed to cultivating a workplace that reflects the diversity of the customers we serve while fostering leadership and innovation.
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Employees engage in a combination of remote and on-site work.
In-person connections is the foundation of Navan, the connections forged through face-to-face interactions improve company culture and what we can achieve together. We operate on a hybrid working model, which we define as four days a week in-office.
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