Senior AI Developer
The Senior AI Developer will productionize and operate multimodal models and reasoning agents for millions of users, building model-serving, evaluation, observability, and reliability infrastructure. The role requires production cloud-services experience, ML systems expertise, and a bachelor's degree.
About the job
Responsibilities
- Deploy and operate LLMs, VLMs, and multimodal models at scale with high reliability, low latency, and cost efficiency.
- Build offline and online evaluation pipelines for reasoning quality, model behavior, hallucination risk, policy compliance, latency, and regression detection.
- Validate and monitor reasoning systems, including long-tail production cases and early exception detection.
- Develop infrastructure for agent orchestration, state management, tool execution, guardrails, and supporting backend services.
- Establish telemetry and observability through dashboards, traces, logs, alerts, and performance analytics.
- Improve throughput, capacity, fallback behavior, model routing, reliability, performance, and infrastructure utilization.
- Evaluate and standardize tools, frameworks, and deployment patterns for LLM and agent operations.
- Implement security controls, access patterns, and operational safeguards for user data and generative AI systems.
Requirements
- Bachelor's degree in Computer Science, Engineering, or a related field.
- 3+ years of experience developing and operating cloud-based services, infrastructure, and APIs in production, ideally on AWS.
- Experience deploying and operating production ML systems, especially LLMs, VLMs, or other large-scale model systems.
- Experience with observability and operational tooling, including monitoring, logging, tracing, and alerting.
- Experience with CI/CD and production deployment workflows across development, staging, and production environments.
Nice to Have
- Agentic development workflows, including AI-assisted coding and review.
- LLM and agent tooling such as LangSmith, LangChain, LangGraph, or MLflow.
- GPU-backed inference systems, model-serving optimization, and latency-sensitive scaling.
- Hosted model APIs such as Anthropic, Gemini, or Bedrock.
- Validation and telemetry systems for generative AI, including regression testing, quality scoring, and production monitoring.
- Containerized services and orchestration technologies such as Docker, Kubernetes, ECS, or EKS.
- Workflow orchestration tools such as Temporal or Step Functions.
- Databricks or similar platforms for data, experimentation, evaluation, or ML platform operations.
- IAM, secrets management, encryption, and compliance-minded cloud controls.
- Infrastructure as code, especially Terraform, and agent infrastructure involving orchestration, tool use, execution control, memory/state handling, and guardrails.
Compensation & Benefits
- Comprehensive medical, dental, and vision coverage.
- Support for gender-affirming care, family and fertility planning, and travel reimbursements where care is not locally accessible.
- Traditional and Roth 401(k) options with a 2% company match.
- Flexible stipends for learning, development, and modern life needs.
Skills
AWS, LLMs, Vlms, Machine Learning, Observability, CI/CD, Langsmith, LangChain, LangGraph, MLflow, Docker, Kubernetes, Terraform, Databricks, Temporal
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