Wand Synthesis AI Inc - Senior Machine Learning Engineer
Requirements
• Hands-on experience building production ML systems integrated with product goals and business logic. • Expertise in ML engineering, agentic workflows, and MLOps practices. • Strong programming skills in Python and experience integrating ML with backend systems and autonomous workflows. • Experience deploying machine learning models at scale, including goal-driven or multi-agent systems. • Experience building ML infrastructure supporting training, experimentation, inference, and agent coordination. • Solid understanding of distributed systems, scalable data pipelines, and real-time agentic decision loops. • Experience designing ML systems on cloud platforms such as AWS, Azure, or GCP. • Experience with highly available model serving systems supporting autonomous agentic tasks. • Strong debugging and troubleshooting skills in complex ML and agentic AI production environments. • Ability to work independently and collaboratively within cross-functional teams. • Experience building ML platforms that enable AI agents to drive product outcomes and autonomous workflows. • Experience with NLP, LLMs, generative AI, or multi-agent systems. • Experience building feature stores or shared ML infrastructure supporting agentic reasoning and coordination. • Experience operating ML workloads on Kubernetes-based infrastructure. • Experience designing systems for real-time goal-driven inference at scale. • Experience building ML systems in enterprise SaaS or large-scale product platforms. • Experience supporting AI capabilities in regulated or enterprise environments. • Experience with large-scale data platforms, streaming architectures, and agent orchestration pipelines. • Experience evaluating ML infrastructure tools for production agentic AI workflows. • Personal Characteristics: • Strong systems thinker who understands interactions across ML, data, infrastructure, agentic workflows, and product logic. • High ownership mentality and accountability for reliable AI systems. • Strong problem solver who anticipates operational, product, and agentic failure modes. • Collaborative mindset with the ability to work across data science, engineering, product, and platform teams. • Learning-oriented, passionate about staying at the forefront of agentic AI and product-driven ML systems. • Calm and methodical when diagnosing complex ML, agentic, or production system issues.
Responsibilities
• Develop and maintain ML platforms and pipelines supporting autonomous, goal-driven AI agents. • Build systems for the full ML lifecycle, including agentic decision-making, task orchestration, and goal execution. • Integrate ML models with product logic and business workflows to operationalize AI capabilities. • Implement pipelines for experimentation, productionization, and continuous agentic learning. • Collaborate with data science and product teams to turn research outputs into production AI agents. • Design and optimize infrastructure for large-scale training, inference, and multi-agent coordination. • Implement observability and monitoring for ML pipelines, agent behaviors, and goal-driven execution. • Build systems for automated evaluation, drift detection, and retraining of AI models. • Ensure reliability, scalability, and operational excellence of ML services powering autonomous workflows. • Troubleshoot complex issues in ML pipelines, agentic systems, and distributed infrastructure. • Contribute to CI/CD and development workflows supporting ML lifecycle, agent orchestration, and model deployment. • Collaborate and share knowledge to improve implementation of agentic AI systems across teams.
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