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Specialized LLM/MLOps skills but mid-level title and metro startup attract many applicants.
Role demands LLM and MLOps expertise, limiting transferability across non-AI domains.
Explicit production LLM, MLOps, cloud, and framework requirements make screening stringent.
Own end-to-end development and production deployment of LLM-powered AI features including RAG pipelines, agentic workflows, and fine-tuning where applicable.
Define and implement benchmarking and evaluation frameworks (offline and online) to measure model quality, performance, cost, and ship readiness.
Build and maintain MLOps infrastructure including model serving, CI/CD, observability, autoscaling, and cost-optimization strategies for production AI systems.
3–6 years of engineering experience with demonstrated production ML/AI system shipping (beyond prototypes or notebooks).
Hands-on experience with LLMs and GenAI including production use of OpenAI, Anthropic, or open-source models, plus practical knowledge of RAG, embeddings, vector stores, and prompt engineering.
Strong MLOps skills including model serving (FastAPI, Triton, SageMaker, or similar), containerization (Docker, Kubernetes), and cloud experience (AWS, GCP, or Azure).
Proficient in Python and familiar with PyTorch or TensorFlow and AI frameworks like LangChain or LlamaIndex.
Experienced in taking complex AI systems from prototype to production independently with strong ownership over live deployments and incident resolution.
Skilled at rigorously benchmarking AI models for multiple KPIs and using data-driven criteria to decide feature readiness for shipping.
Engineering-focused mindset emphasizing testing, observability, API design, and cost-performance optimization within production AI services.