Senior AI Solutions Engineer

About the role

Fiddler is an AI observability and control plane platform that helps organizations deploy trustworthy AI solutions. Our mission is to ensure our customers achieve meaningful, measurable outcomes from their AI observability investments.

Responsibilities

• Drive successful Proof-of-Value experiences.
• Partner closely with a Fiddler go-to-market teams to architect and implement AI solutions, aligning prospect goals with Fiddler's capabilities, and ensuring evaluation cycles 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 AI 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 AI lifecycle.
• Deliver seamless integrations and technical success. Write custom integration code that connects Fiddler to customer data ecosystems using tools such as 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 AI 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.

Requirements

• 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 AI 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 Sales—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.

Qualifications

• Understanding of data science concepts, model interpretability, and explainability techniques used in modern ML systems.
• History of direct support of government or governmental agencies/programs.
• Experience building GenAI Applications and Agentic workflow applications.
• 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.

Skills

• Understanding of data science concepts, model interpretability, and explainability techniques used in modern ML systems.
• History of direct support of government or governmental agencies/programs.
• Experience building GenAI Applications and Agentic workflow applications.
• 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.

Benefits

• Competitive pay + equity
• Premium health, dental & vision (100% premium coverage for employees)
• Open PTO
• 401(k) plan
• Monthly fitness reimbursement
• Paid parental leave
• Palo Alto HQ Vibes
• Annual Caltrain pass
• Monthly in-office massages
• Fastrak reimbursement
• Lunch provided Mon–Thurs