Generative AI Engineer
đź“Ť 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
đź› 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
đź’ˇ
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.