What are the responsibilities and job description for the Full Stack Engineer position at Heitmeyer Consulting?
Job Title: Full Stack Engineer
Location: Hybrid (Philadelphia/Charlotte/Ft Lauderdale/Scarborough)
Position Overview:
Heitmeyer Consulting is currently hiring a Full Stack Engineer to support work at large, East-Coast based bank within their AI Cybersecurity Team.
Depth & Scope:
Using Azure AI services to deploy AI models/applications – Azure OpenAI, Azure Machine Learning, Azure AI Studio Full Stack Engineering background
Location: Hybrid (Philadelphia/Charlotte/Ft Lauderdale/Scarborough)
Position Overview:
Heitmeyer Consulting is currently hiring a Full Stack Engineer to support work at large, East-Coast based bank within their AI Cybersecurity Team.
Depth & Scope:
- Linux, Windows Sys Admin, Middleware, disciplines, Network Engineering and some development
- Someone who can truly navigate the full stack
- Understands hardware to application layer
- How to wire AI engines in to this environment, how to build the architecture
- 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)
