Key Responsibilities

Build and maintain production-grade ML pipelines for data collection, training, evaluation, and deployment.

Instrument products to capture user interactions and human corrections as structured datasets for continuous model improvement.

Design and implement evaluation harnesses to measure diagnostic accuracy, root-cause correctness, and agentic workflows.

Fine-tune and distill open-weight foundation models (Qwen, Llama, and similar) to improve performance while reducing inference cost.

Deploy, optimize, and scale inference workloads using vLLM or equivalent serving frameworks.

Develop AI solutions for cloud, on-premises, and air-gapped environments while ensuring security and compliance.

Optimize model latency, throughput, and infrastructure utilization for large-scale production deployments.

Collaborate with Product, Engineering, and Research teams to integrate AI capabilities into customer-facing products.

Leverage AI-assisted development tools such as Claude Code or equivalent CLI-based coding environments to accelerate engineering workflows.

Required Qualifications

10+ years of software engineering or machine learning experience.

Hands-on experience building and operating production ML systems end-to-end.

Strong expertise in data engineering, model training, evaluation, deployment, and monitoring.

Proven experience fine-tuning and distilling open-weight LLMs such as Qwen and Llama.

Experience designing evaluation frameworks for correctness-sensitive, agentic, and tool-using AI systems.

Expertise in deploying and scaling inference using vLLM or comparable inference engines.

Proficiency with Python, PyTorch, Hugging Face Transformers, and modern ML frameworks.

Experience with Kubernetes, Docker, Linux, and MLOps best practices.

Strong command-line development experience and proficiency with AI coding tools such as Claude Code, Cursor, or similar.

Excellent problem-solving, communication, and cross-functional collaboration skills.