Job description
About Workato
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
Responsibilities
We are looking for an exceptional Senior ML/AI Engineer who can take ownership of building and operating AI services that rely on large‑language models (LLMs) and custom machine‑learning models. You will join a cross‑functional team creating AI agents and chat‑based “copilot” experiences within the Workato ecosystem. Our work emphasizes LLMOps/MLOps principles: efficient deployment, monitoring and continuous improvement of ML services. We fine‑tune and prompt‑engineer existing models rather than training models from scratch.
Build and enhance AI services. Design, build and extend AI services using foundation LLMs and custom models. Collaborate with product managers and researchers to translate high‑level requirements into robust Python services.
Fine‑tune and adapt models. Select foundation models and fine-tune them for specific downstream tasks; prepare and curate training datasets; experiment with prompt engineering and embeddings to improve model outputs. LLMOps covers data preparation, model training, monitoring, fine‑tuning and deployment.
Develop and operate ML/LLM pipelines. Implement end‑to‑end pipelines for model evaluation, retraining, and deployment.
Production deployment & integration. Create and maintain APIs and microservices for model serving; build and maintain middleware layers integrating LLMs with existing systems.
Monitoring & evaluation. Establish monitoring for latency, throughput, hallucination rate, accuracy and cost; build dashboards; measure the quality of chat agents and copilots through metrics and A/B testing.
Quality, feedback and continuous improvement. Implement feedback loops with end‑users and perform A/B tests to compare prompts and models. Assist with model evaluation frameworks using LLM‑specific metrics (e.g., BLEU, ROUGE).
Software engineering excellence. Write well‑designed, testable, efficient Python code; review peers’ code.
Cross‑team collaboration. Work with infrastructure, data science and product teams; participate in design discussions and propose improvements to existing services. LLMOps depends on cooperation among data scientists, DevOps engineers and other IT teams.
Requirements
Minimum qualifications
Bachelor’s or Master’s degree in computer science, engineering, information systems or equivalent experience.
5+ years of experience as a Machine‑Learning engineer or AI engineer, including deploying ML services in production.
Expert proficiency in Python and readiness to work with multiple programming languages when needed (e.g., Go or Ruby for integrations).
Hands-on experience with MLOps/LLMOps practices such as CI/CD pipelines, containerization (Docker/Kubernetes), experiment tracking (MLflow, Weights & Biases, Kubeflow) and model monitoring.
Understanding of LLMOps components—data preparation, fine‑tuning, monitoring and deployment.
Experience building and operating APIs and microservices for AI models, including instrumentation for performance and cost monitoring.
Ability to develop evaluation metrics and benchmark tests; knowledge of LLM evaluation metrics (BLEU, ROUGE) is a plus.
Familiarity with AI safety, bias detection and regulatory compliance.
Preferred skills
Knowledge of agentic architectures and multi‑agent systems; experience building chat‑based agents or copilots.
Experience with open‑source LLM frameworks (e.g., LangChain, LlamaIndex) and vector databases.
Familiarity with metrics logging and monitoring stacks (Prometheus, ELK), and observability best‑practices.
Soft Skills / Personal Characteristics
Strong written and spoken English; ability to communicate complex ideas clearly to technical and non‑technical stakeholders.
Collaborative team player who takes initiative and thrives in a dynamic startup environment.
Adaptable mindset with willingness to switch tools or languages when needed.
Analytical thinker with a focus on continuous improvement and innovation.
