Senior AI Operations (AI Ops) Engineer

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.

• 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.

How does Navan ensure its pay and bonus plans are competitive?

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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