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Workato transforms technology complexity into business opportunity. As the leader in enterprise orchestration, Workato helps businesses globally streamline operations by connecting data, processes, applications, and experiences. Its AI-powered platform enables teams to navigate complex workflows in real-time, driving efficiency and agility.
Trusted by a community of 400,000 global customers, Workato empowers organizations of every size to unlock new value and lead in today’s fast-changing world. Learn how Workato helps businesses of all sizes achieve more at workato.com.
Why join us?Ultimately, Workato believes in fostering a flexible, trust-oriented culture that empowers everyone to take full ownership of their roles. We are driven by innovation and looking for team players who want to actively build our company.
But, we also believe in balancing productivity with self-care. That’s why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives.
If this sounds right up your alley, please submit an application. We look forward to getting to know you!
Also, feel free to check out why:
Business Insider named us an “enterprise startup to bet your career on”
Forbes’ Cloud 100 recognized us as one of the top 100 private cloud companies in the world
Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America
Quartz ranked us the #1 best company for remote workers
We are looking for an experienced and exceptional Senior AI Engineer to join our growing team. In this role, you will be involved in the design, development, and optimization of AI and Machine Learning solutions that deliver exceptional user experiences. The ideal candidate will combine strong software engineering skills with deep knowledge of machine learning systems.
As part of this role you will:
Design and implement advanced AI/ML systems with a focus on LLMs, embeddings, and retrieval-augmented generation (RAG) architectures.
Develop and optimize information retrieval solutions including vector databases, semantic search, and knowledge graph implementations.
Build production-grade AI pipelines for data processing, model training, fine-tuning, and serving at scale.
Create and maintain graph-based knowledge systems that enhance LLM capabilities through structured relationship data.
Implement and optimize hybrid RAG architectures combining traditional search, vector embeddings, and knowledge graphs.
Lead technical initiatives for AI system integration into existing products and services.
Collaborate with data scientists and ML researchers to implement and productionize new AI approaches and models.
Bachelor's degree in Computer Science or a related field, or equivalent practical experience.
5+ years in backend software development using modern programming languages (e.g., Python (strongly preferred!), Golang or Java).
Demonstrated experience building production systems with LLMs (OpenAI, Anthropic, open-source models) including prompt engineering and fine-tuning.
In-depth knowledge of vector databases and embedding models for semantic search and retrieval.
Experience implementing RAG architectures with various retrieval strategies (sparse, dense, hybrid) and context optimization techniques.
Proficiency with knowledge graph technologies (Neo4j, Neptune, or similar) and graph-based information retrieval.
Strong background in information retrieval systems including BM25, TF-IDF, and modern neural search approaches.
Experience with AI/ML infrastructure including containerization, orchestration, and scaling of model inference.
Expertise in cloud platforms' AI offerings (AWS Bedrock, Azure OpenAI, Vertex AI) and their integration patterns.
Familiarity with model optimization techniques including quantization, distillation, and efficient serving strategies.
Experience with streaming data processing for real-time AI applications using technologies like Kafka, Kinesis, or similar.
Proficiency with AI observability and evaluation tools for tracking model performance, drift, and quality.
Demonstrated ability to balance technical innovation with production reliability when implementing cutting-edge AI systems.
Strong communication abilities to explain technical concepts
Collaborative mindset for cross-functional team work
Detail-oriented with strong focus on quality
Self-motivated and able to work independently
Passion for solving complex search problems
