AI/ML Full Stack Engineer / Azure Open AI (W2 CTH at PA/NC/FL/ME) – HYBRID

Job Overview

logoJob Title: AI/ML Full Stack Engineer/ Azure Open AI
Location: Hybrid/Onsite (Philadelphia – PA /Charlotte – NC /Fort Lauderdale – FL/Scarborough – ME)
Job Type: W2 Contract to Hire (CTH)
Position Overview:Client is currently hiring a Full Stack AI/ML Engineer to support work at client’s large AI Cybersecurity Team.
Depth & Scope:· Using Azure AI services to deploy AI models/applications – Azure Open AI, Azure Machine Learning, Azure AI Studio· Full Stack Engineering backgroundo Linux, Windows Sys Admin, Middleware, disciplines, Network Engineering, and some developmento Someone who can truly navigate the full stacko Understands hardware to application layero How to wire AI engines into this environment, how to build the architectureo Deployments in Azure
· Strong Prompt Engineering, Python Development and REST API skills· Understanding of different GenAI LLMs, development frameworks (Langchain, Semantic Kernel), and AI ecosystem tooling (evaluation, monitoring, governance, security, vector databases)· Ability to identify and design approaches for creating guardrails to mitigate GenAI security/cyber risks· Deep understanding of major components of RAG applications, usage patterns, and optimization· LLM Fine-tuning approaches· Understanding the full lifecycle of AI model development, deployment· Tools for support large scale Inferencing· Solid understanding of machine learning algorithms, neural network architectures, and LLM model training processes· Knowledge of different Agentic frameworks· Familiarity with g Azure AI services – Azure OpenAI, Azure Machine Learning, Azure AI Studio· Solid understanding of machine learning algorithms, neural network architectures, and AI model training processes· Experience with adversarial machine learning techniques and familiarity with common attack vectors against AI systems· Familiarity with AI ethics and bias in AI systems· Knowledge of techniques for interpreting and explaining AI model decisions· Ability to identify and design approaches for creating guardrails to mitigate GenAI security/cyber risks & adversarial attacks· Knowledge of OWASP and NIST GenAI security guidelines & policies· Understanding the different areas of evaluation for a RAG system· LLM model evaluation tooling (e.g. Using LLMs-as-a-judge)

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