Builds and deploys production-scale AI/ML systems using LLMs, from fine-tuning and evaluation to low-latency infrastructure. Requires 5+ years experience with PyTorch/TensorFlow, MLOps, AWS, and taking models to production at high-growth startups.
200k – 250k/yr
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About the role
Responsibilities
Own the complete lifecycle of large language model implementation: from data preparation and fine-tuning through rigorous evaluation and production deployment.
Develop automated evaluation frameworks that continuously assess model accuracy, identify edge cases, and quantify improvements across iterations.
Work directly with product managers and engineers to integrate AI as a core product capability.
Shape our AI roadmap by staying current with industry developments, evaluating emerging techniques, and making pragmatic adoption decisions.
Design and implement low-latency, high-throughput, cloud-based AI/ML systems capable of handling thousands of requests per second.
Build the foundational infrastructure - model serving, monitoring, deployment pipelines, and automated testing frameworks - that enables rapid experimentation and iteration while maintaining production reliability.
Requirements
5-7+ years of engineering experience with demonstrated hands-on knowledge of applying LLMs and agents in industry.
Experience at a high-growth startup building machine learning infrastructure from the ground up.
Demonstrated ability to take models from research/experimentation through production deployment at scale.
Fluency in Python and related AI/ML frameworks (TensorFlow, PyTorch, Keras, etc.).
Hands-on experience with LLMs and contemporary AI engineering patterns: RAG architectures, embedding models, vector databases, prompt engineering, and fine-tuning strategies.
Curious, systematic, and execution-oriented—you don't wait for perfect requirements and can navigate technical tradeoffs independently.
Strong foundation in MLOps: CI/CD for ML, model versioning, monitoring, and observability.
Strong technical background in AWS cloud architecture and automated infrastructure provisioning with Terraform.
Nice-to-haves
Experience with agentic frameworks like LangChain.
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