Solutions Architect, Customer Success - US (Remote)

As a Solutions Architect, Customer Success at Fiddler, your mission is to ensure our customers achieve meaningful, measurable outcomes from their AI observability investments. You serve as both a technical expert and trusted advisor, bridging the gap between complex ML/LLM systems and real-world business value. By guiding customers through onboarding, building seamless integrations, and championing their needs across the organization, you help them operationalize trustworthy AI at scale while fueling Fiddler’s growth through successful adoption, renewals, and expansion.

You’ll join a tight-knit, highly collaborative Customer Success Engineering team that partners closely with some of the world’s leading enterprise AI organizations. We operate remotely but stay deeply connected through constant communication, collaboration, and shared purpose. Our team thrives on knowledge sharing, peer learning, and collective problem solving; no one works in a silo.

We celebrate each other’s successes, support one another through complex challenges, and take pride in helping our customers achieve real-world impact with Fiddler’s AI Observability platform. Every project is a team effort, and every win is shared. If you love working alongside smart, driven peers who genuinely care about both customer success and each other’s growth, you’ll feel right at home here.

Drive successful onboarding experiences. Partner closely with a Fiddler Delivery Manager to architect and implement onboarding solutions, aligning customer goals with Fiddler’s capabilities, and ensuring deployments are delivered on time, with precision, and with clear success criteria met.

Be the trusted technical partner our customers rely on. You’ll build strong relationships with data science and ML engineering teams, guiding them through every stage of their AI observability journey and ensuring they realize measurable value from Fiddler.

Champion the customer voice across Fiddler. Lead ongoing technical engagements, status syncs, roadmap discussions, QBRs, and escalation management; to ensure customer feedback influences our product roadmap and long-term strategy.

Become a domain expert in AI Observability. Master Fiddler’s platform and help customers operationalize observability best practices improving model transparency, performance monitoring, and compliance across their ML lifecycle.

Deliver seamless integrations and technical success. Write custom integration code that connects Fiddler to customer data ecosystems using tools like Snowflake, Airflow, MLflow, S3, Kafka, and more; ensuring robust, scalable, and secure pipelines.

Accelerate platform adoption. Build and refine integration patterns between Fiddler and common data platforms, workflow tools, and ML infrastructures to reduce time-to-value for new customers.

Uncover and drive expansion opportunities. Identify new ways Fiddler can provide impact; whether through advanced observability use cases, expanded integrations, or deeper model governance, helping drive renewals and growth.

Bachelor’s degree in Computer Science (AI/ML focus), Statistics, Mathematics, or related field with 5-7+ years of professional experience.

2+ years of hands-on experience deploying, monitoring, or maintaining ML models in production environments.

Excellent communication, presentation, and storytelling abilities; able to distill complex technical concepts into clear, actionable insights for both technical and executive audiences.

Strong organizational and project management skills, with the ability to balance multiple customer engagements and priorities.

Demonstrated collaboration across cross-functional teams—Product, Engineering, and Delivery—to drive customer outcomes.

A customer-first mindset with empathy, curiosity, and a deep sense of ownership for delivering value.

Passion for continuous learning and a desire to inspire customers and peers through thought leadership and technical credibility.

Understanding of data science concepts, model interpretability, and explainability techniques used in modern ML systems.

Working knowledge of data and workflow tools such as Hadoop, MongoDB, Snowflake, BigQuery, Spark, Kafka, Kinesis, RabbitMQ, Airflow, MLflow, Luigi, Kubeflow, or Argo.

Experience with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.

Proficiency with Kubernetes and cloud platforms (AWS, Azure, GCP).

Familiarity with the ML/DS lifecycle, including feature generation, model training, deployment, monitoring, and evaluation (batch and real-time scoring via REST APIs).

Understanding of emerging AI technologies—Generative AI, Large Language Models (LLMs), RAG architectures, and agent-based systems. For candidates in the San Francisco Bay Area, this role is a hybrid position requiring working from our Palo Alto office 3 days a week.