Generative AI Engineer

Generative AI Engineer

📍 Frederick, MD (Onsite)

💰 $100,000 – $115,000 + Benefits

🧠 Enterprise-Grade LLM Systems | Multi-Agent Architectures | Production AI

🕒 Full-Time W2 | 8–10+ Years Engineering Experience

🔥 This Is Not a Prompt Engineering Role

We are looking for a
serious AI engineer
who has already built and deployed production-grade LLM systems.

If your experience is limited to experimenting with ChatGPT wrappers or surface-level RAG demos, this role is not for you.

If you have designed multi-agent workflows, optimized memory architectures, mitigated hallucinations in production, and deployed scalable AI services, keep reading!

🧠 What You’ll Own

• Design and deployment of enterprise LLM applications
• Production-grade RAG pipelines using vector databases
• Autonomous agentic systems capable of reasoning & planning
• Multi-agent orchestration (planner, executor, critic, evaluator)
• Evaluation frameworks (accuracy, latency, safety, alignment)
• Responsible AI guardrails & traceability design

This role sits at the core of building scalable, trustworthy AI capability inside a complex enterprise environment

⚙️ What Strong Candidates Already Have

You have:

• Built LLM systems using frameworks like LangChain or similar
• Designed RAG architectures with embeddings + vector databases
• Built or customised autonomous agent frameworks
• Deployed containerised AI services (Docker, Kubernetes)
• Worked with Azure ML or another hyperscaler
• Designed APIs and microservices around AI systems
• Implemented evaluation & hallucination mitigation strategies

You understand:

• Prompt engineering at system level (not surface level)
• Memory management strategies in agentic systems
• LLM safety, guardrails & alignment
• Performance optimisation and production constraints

🛠 Core Tech Stack

• Python (expert level)
• Transformers, PyTorch / TensorFlow
• Vector databases
• Docker & Kubernetes
• Azure (preferred) / AWS / GCP

🧩 What We’re Really Looking For

• Engineers who build before they talk
• People who experiment with emerging research and apply it pragmatically
• Builders comfortable with ambiguity
• Individuals who can turn abstract business use cases into autonomous AI systems

💡
Why This Role Is Different

You won’t be maintaining legacy ML models.

You’ll be engineering intelligent systems that:

• Plan multi-step workflows
• Use tools autonomously
• Integrate with enterprise APIs
• Operate safely at scale

This is hands-on, technical, and high ownership.

If you’ve already deployed multi-agent workflows into production…

If you’ve built evaluation pipelines to measure LLM drift…

If you’ve solved hallucination issues beyond prompt tweaking…

Apply.