AI Engineer

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 templates/libraries; implement prompt testing frameworks; establish prompt versioning/governance.

Evaluate solutions for quality/safety/latency/cost 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.

Levels

We’re hiring across multiple levels (entry/junior through senior/principal).


How You’ll Work

You’ll collaborate in agile delivery pods alongside product, architecture, engineering, and ops roles to build and scale agentic AI solutions.
Projects may include discovery workshops, rapid prototyping, and production pilots with measurable outcomes.

Apply

Send your application (or resume) and include:

• 1–2 examples of LLM apps/agentic systems you built (even internal or side projects)
• The platform(s) you used (e.g., Bedrock / Vertex / Azure) and how you handled RAG, evaluation, and production concerns