AI Solutions Engineer

Role Description

We are looking for a client-obsessed AI Solutions Engineer with entrepreneurial tendencies to join the good fight and help build out our Solutions Engineering org. You’ll be the trusted technical advisor for our customers, driving business value, offering advice, and growing accounts. You’ll accomplish this by:

• Working closely with some of the most sophisticated ML / GenAI teams in the world.
• Acting as a trusted advisor to our customers, while also building relationships with technical and business stakeholders.
• Advising on GenAI and ML best practices.
• Giving ML and LLM product demos to technical and business stakeholders.
• Running strategic business reviews for customers in partnership with our sales team.
• Interfacing with our pre-sales engineering team to gather client goals and KPIs.
• Partnering with our product and engineering teams to help drive the product roadmap.
• Spearheading new opportunities within existing accounts to help drive expansions.

Qualifications

• Previous experience working as a Data Scientist, Machine Learning Engineer, or as an Engineer working with ML models or GenAI applications in production.
• Comfortable working in public Cloud environments (AWS, Azure, GCP).
• Knowledge of machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Knowledge of LLM / Agentic frameworks such as Llamaindex, LangGraph, and DSPy.
• Understanding of ML/DS concepts, model evaluation strategies, and lifecycle (feature generation, model training, model deployment, batch and real-time scoring via REST APIs) and engineering considerations.
• Understanding of GenAI concepts and application evaluation + development lifecycle.
• Proficiency in a programming language (Python, JS/TS, Java, Go, etc).
• Strong communication skills - ability to simplify complex, technical concepts.
• A quick and self-learner - undaunted by technical complexity of production ML deployments and welcome the challenge to learn about them and develop your own POV.

Requirements

• Previous engineering experience in:
• Data Science
• MLOps
• ML Frameworks
• LLM / Agentic frameworks
• Customer-facing experience strongly preferred such as Solutions Architect, Implementation Specialist, Sales Engineer, Customer Success Engineer, Consultant, or Professional Service roles.
• Prior experience working with applications deployed with Kubernetes.
• Prior experience demoing technical products to both business and technical audiences.

Benefits

• The estimated annual salary and variable compensation for this role is between $125,000 - $175,000, plus a competitive equity package.
• Total compensation includes a comprehensive benefit package, including:
• Medical, dental, vision
• 401(k) plan
• Unlimited paid time off
• Generous parental leave plan
• Others for mental and wellness support
• WFH monthly stipend to pay for co-working spaces for remote employees.