designed to help clients overcome challenges and achieve their goals through an agile, modular, and bespoke approach. We’re a collective of curious, seasoned consultants who build meaningful connections and deliver with purpose and authenticity.
Why we’re hiring
Demand for GenAI and Agentic AI solutions is accelerating across our client pipeline, and we’re proactively building a strong bench of AI Engineers who can help architect, build, and scale real production systems — not demos.
The Role
As an
AI Engineer (GenAI / Agentic AI) , you’ll design and build
LLM-powered applications and agentic systems
that plan, reason, and take action — often by securely interacting with enterprise systems and APIs.
You’ll work across discovery → rapid prototypes → pilot and scale, with a strong emphasis on
quality, safety, latency, cost, and user feedback loops .
Location Requirement
Candidates must currently reside in the Greater San Francisco, Greater Seattle, Greater Chicago, or DFW metro areas. Relocation (now or in the future) is not supported for this role.
Please note:
We are unable to sponsor or transfer visas for this position . You must be authorized to work in the United States for any employer without requiring sponsorship or visa transfer now or in the future.
Please no resumes from third-party agencies or recruiters
What You'll Do
You may work across several of these areas depending on level and project:
Build GenAI / LLM applications
Design and develop LLM-powered applications using enterprise AI platforms (e.g., AWS Bedrock, Azure OpenAI / Azure AI platforms, Google Vertex AI).
Implement multi-step orchestration workflows that translate user intent into reliable actions and explainable outputs.
Build robust
RAG pipelines
(vector databases, embeddings, chunking strategies) and validate grounding quality.
Engineer agentic solutions (Plan → Reason → Execute → Feedback)
Design agent reasoning/control patterns (e.g., planning vs execution separation, tool calling, memory/context management).
Integrate agents with tools/APIs and enterprise workflows with appropriate governance and guardrails.
Prompt engineering + evaluation
Create reusable prompt
and iterate quickly.
Production readiness + operations
Partner with platform/LLMOps teammates to deploy, monitor, and improve LLM systems in production.
Build observability and reliability mechanisms for agent-based workflows.
Client-facing consulting
Lead technical discovery, map workflows/pain points, and communicate solutions to technical and executive stakeholders.
What We're Looking For (Must-Have)
1–2+ years hands-on GenAI / Agentic AI experience building LLM apps
on enterprise platforms (AWS Bedrock / Vertex AI / Azure AI platforms) in a professional setting.
Strong
backend engineering
experience (Python preferred) delivering production-grade systems.
Hands on
professional experience
with
RAG
patterns and implementation.
Ability to communicate clearly and contribute in fast-moving, cross-functional teams.
Computer Science / strong CS fundamentals
Nice-to- Have/Differentiators
Applied Scientist style skills: deep learning/NLP with PyTorch/TensorFlow + Hugging Face; ability to interpret research and implement emerging techniques.
Fine-tuning and optimization methods (LoRA/PEFT/QLoRA),
distillation/quantization/pruning,
GPU memory optimization.
Experience building secure tool integrations / agent middleware (tool schemas, SaaS integrations like
Salesforce/SAP/ServiceNow,
OAuth2, API security).
Evaluation harnesses and regression testing for prompts/agents; RAG quality testing.
Cloud-native experience in large enterprise environments.
This is a broad range intended to accommodate varying levels of experience, with final compensation determined by demonstrated, hands‑on technical capability and real‑world impact.