AI/ML Full Stack Engineer

4 days ago


Philadelphia, United States Cyberollie Full time

Job 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 background

o Linux, Windows Sys Admin, Middleware, disciplines, Network Engineering, and some development

o Someone who can truly navigate the full stack

o Understands hardware to application layer

o How to wire AI engines into this environment, how to build the architecture

o 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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