AI Engineer

United States·Seattle,SeattleFull-Timemid

Data ScienceMachine Learning EngineerAI EngineerDataData & AI

5 views0 saves0 applied

Apply Now

Quick Summary

Overview

Unify Consulting is a leading AI management consulting firm designed to help clients overcome challenges and achieve their goals through an agile, modular, and bespoke approach.

Key Responsibilities

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.

Technical Tools

awsazurehuggingfaceopenaipythonpytorchsalesforcetensorflowagiledeep-learningsaas

Unify Consulting is a leading AI management consulting firm 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.

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.

Requirements

~1 min read

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.

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.

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

Evaluate solutions for quality/safety/latency/cost and iterate quickly.

Partner with platform/LLMOps teammates to deploy, monitor, and improve LLM systems in production.

Build observability and reliability mechanisms for agent-based workflows.

Lead technical discovery, map workflows/pain points, and communicate solutions to technical and executive stakeholders.

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

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.

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

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.

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