Senior ML Engineer

Summary

Join Workato as a Senior ML/AI Engineer and build and operate AI services using large language models (LLMs) and custom machine learning models. You will collaborate with a cross-functional team to create AI agents and chat-based "copilot" experiences within the Workato ecosystem. This role emphasizes LLMOps/MLOps principles, focusing on efficient deployment, monitoring, and continuous improvement of ML services. You will fine-tune and prompt-engineer existing models rather than training from scratch. Workato offers a flexible, trust-oriented culture and a multitude of benefits. The company is recognized for its innovation and is a leader in enterprise orchestration.

Requirements

• 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

Responsibilities

• 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

Preferred Qualifications

• 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
• 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