T
Agentic AI Engineer
Tactical Edge
9 hours ago
Full-time
On-site
We're looking for an engineer who builds autonomous AI systems that reason, plan, and execute in production environments. You'll design multi-agent architectures, orchestration pipelines, and tool-use frameworks that power our enterprise AI solutions.
This is not a research role — this is a production engineering role where your agents run 24/7 in enterprise environments.
Key Responsibilities
Design and build multi-agent systems using frameworks like LangGraph, CrewAI, or custom orchestration.
Implement tool-use, function calling, and agentic workflows that operate reliably in production.
LLM Integration & Optimization
Work with models from Anthropic, OpenAI, and open-source (Llama, Qwen, Mistral).
Optimize prompts, manage context windows, implement RAG pipelines, and fine-tune models for domain-specific tasks.
Production Systems
Build agents that run 24/7 in enterprise environments with proper error handling, fallback strategies, human-in-the-loop controls, and observability.
Deploy on AWS (Bedrock, SageMaker, ECS).
Evaluation & Testing
Design eval frameworks to measure agent quality, accuracy, and safety.
Build automated testing for agentic workflows including regression testing and red‑teaming.
Security & Governance
Implement guardrails, output validation, and audit logging for AI agents operating in regulated industries.
Self-directed builder who ships without hand-holding.
Strong Python skills with deep understanding of async patterns and systems design.
Understands LLM internals (not just API wrappers) — tokenization, attention, context management.
Production-minded: thinks about reliability, observability, and failure modes from day one.
Comfortable with ambiguity and evolving requirements.
Preferred Qualifications
3+ years building with LLMs in production environments.
Experience with agentic frameworks (LangGraph, CrewAI, AutoGen, or custom).
Strong Python and systems programming skills.
Familiarity with AWS AI services (Bedrock, SageMaker, ECS).
Understanding of prompt engineering, RAG architectures, and retrieval systems.
How We Work Outcome-driven
Enterprise-first
Agentic by design
Systems that reason and act safely
Small teams, high ownership
Autonomy with accountability
What You'll Get
Work on real, production AI deployments
Enterprise-scale challenges and measurable impact
Cross-functional collaboration and high ownership
Flexible work setup where applicable
Hiring Process
Intro call
Fit + context
Technical deep dive
Agent architecture, LLM systems, production design
System design exercise
Multi-agent workflow design
Final conversation
Alignment + next steps
We value clarity, ownership, and thoughtful execution over buzzwords.
#J-18808-Ljbffr
This is not a research role — this is a production engineering role where your agents run 24/7 in enterprise environments.
Key Responsibilities
Design and build multi-agent systems using frameworks like LangGraph, CrewAI, or custom orchestration.
Implement tool-use, function calling, and agentic workflows that operate reliably in production.
LLM Integration & Optimization
Work with models from Anthropic, OpenAI, and open-source (Llama, Qwen, Mistral).
Optimize prompts, manage context windows, implement RAG pipelines, and fine-tune models for domain-specific tasks.
Production Systems
Build agents that run 24/7 in enterprise environments with proper error handling, fallback strategies, human-in-the-loop controls, and observability.
Deploy on AWS (Bedrock, SageMaker, ECS).
Evaluation & Testing
Design eval frameworks to measure agent quality, accuracy, and safety.
Build automated testing for agentic workflows including regression testing and red‑teaming.
Security & Governance
Implement guardrails, output validation, and audit logging for AI agents operating in regulated industries.
Self-directed builder who ships without hand-holding.
Strong Python skills with deep understanding of async patterns and systems design.
Understands LLM internals (not just API wrappers) — tokenization, attention, context management.
Production-minded: thinks about reliability, observability, and failure modes from day one.
Comfortable with ambiguity and evolving requirements.
Preferred Qualifications
3+ years building with LLMs in production environments.
Experience with agentic frameworks (LangGraph, CrewAI, AutoGen, or custom).
Strong Python and systems programming skills.
Familiarity with AWS AI services (Bedrock, SageMaker, ECS).
Understanding of prompt engineering, RAG architectures, and retrieval systems.
How We Work Outcome-driven
Enterprise-first
Agentic by design
Systems that reason and act safely
Small teams, high ownership
Autonomy with accountability
What You'll Get
Work on real, production AI deployments
Enterprise-scale challenges and measurable impact
Cross-functional collaboration and high ownership
Flexible work setup where applicable
Hiring Process
Intro call
Fit + context
Technical deep dive
Agent architecture, LLM systems, production design
System design exercise
Multi-agent workflow design
Final conversation
Alignment + next steps
We value clarity, ownership, and thoughtful execution over buzzwords.
#J-18808-Ljbffr