AI Solutions Engineer
Remote
|
Full Time
|
Customer Success Team
We're searching for a
client-obsessed AI Solutions Engineer
with an entrepreneurial mindset and a passion for helping teams succeed with GenAI and ML applications.
In this role, you'll act as a
trusted advisor
— guiding technical teams and business stakeholders alike, shaping best practices, and driving adoption across some of the world's most advanced AI organizations.
You'll work at the intersection of engineering and customer success, helping users deploy, monitor, and optimize cutting-edge AI and agentic systems — all while influencing the product roadmap and enabling customers to scale with confidence.
What You'll Do
- Partner with some of the most sophisticated ML and GenAI teams globally.
- Act as a trusted technical advisor and strategic partner for enterprise clients.
- Deliver engaging product demos and business reviews to technical and non-technical audiences.
- Consult on best practices for GenAI and ML deployment and lifecycle management.
- Collaborate with internal sales, product, and engineering teams to deliver value and drive growth.
- Identify and spearhead new opportunities within existing accounts.
What You Bring
(You don't need to tick every box — if this excites you, we'd love to hear from you)
- Hands-on experience as a
Data Scientist
,
Machine Learning Engineer
, or similar technical role.
- Strong understanding of
ML/DS concepts
, evaluation strategies, and deployment lifecycles.
- Exposure to
LLM/Agentic frameworks
(e.g., LangGraph, LlamaIndex, DSPy).
- Proficiency in
Python
,
JS/TS
,
Java
, or
Go
.
- Familiarity with
cloud platforms
(AWS, Azure, GCP).
- Excellent communication skills with the ability to bridge technical and business conversations.
- A proactive learner who thrives in technically complex environments.
Bonus Points
- Experience in
MLOps
,
LLM/Agentic frameworks
, or
data science tooling
.
- Prior
customer-facing experience
(Solutions Architect, Customer Success Engineer, Consultant, etc.).
- Comfortable demoing technical products and working with enterprise clients.
- Familiarity with
Kubernetes
or modern deployment environments.