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Mid-level role in a metro with practical GenAI production demand yields moderate applicant competition.
Role requires specialized production GenAI/MLOps and Azure experience, limiting cross-industry transferability.
Multiple explicit requirements (3–4 years, 2 years GenAI, Azure stack, MLOps tooling) create strict filtering.
Own end-to-end development, deployment, and operation of ML and LLM-powered AI applications on Azure, ensuring metrics like accuracy, cost, latency, safety, drift, and uptime are managed post-launch.
Build and maintain MLOps/LLMOps pipelines including CI/CD, model/prompt versioning, monitoring, rollback mechanisms, and observability tooling for live AI systems.
Design and implement guardrails for production AI safety including content filtering, prompt-injection defense, PII redaction, and human-in-the-loop checkpoints, plus architect RAG and agentic systems with vector stores and orchestration frameworks.
3–4 years of hands-on software/ML engineering experience, with at least 2 years deploying GenAI/LLM applications to production.
Proficient in core AI/ML fundamentals and tools: scikit-learn, XGBoost/LightGBM, pandas, NumPy, and at least one deep learning framework (PyTorch or TensorFlow).
Strong working knowledge and experience with Azure AI/ML stack, including Azure Machine Learning, Azure OpenAI Service, and Azure App Service/Functions deployment.
Experience with MLOps/LLMOps tooling (CI/CD pipelines, Docker, versioning, experiment tracking), production safety guardrails implementation, Python production-grade coding, and orchestration frameworks (LangGraph, Semantic Kernel, LangChain, or similar). Notice period: Not explicitly mentioned in the JD.
Demonstrated capability managing AI application lifecycle end-to-end with focus on production deployment maturity over research depth.
Experienced at handling operational challenges such as model hallucination, drift, cost control, rollback strategies, and guardrail redesign post-production.
Strong strategic understanding of when to use classical ML vs. LLM approaches, with cross-functional collaboration experience translating stakeholder needs into deployable AI features.