Wand Synthesis AI Inc - Staff Machine Learning Engineer
Requirements
• Extensive hands-on experience building production ML systems integrated with product goals and business logic. • Deep expertise in agentic AI, ML engineering, and MLOps practices. • Strong programming skills in Python and experience integrating ML with backend systems and autonomous workflows. • Proven experience deploying machine learning models at scale, including goal-driven or multi-agent systems. • Experience building ML infrastructure for training, experimentation, inference, and agent coordination. • Strong 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 building highly available model serving systems supporting autonomous agentic tasks. • Ability to influence architecture and product integration decisions across engineering teams. • Strong debugging and troubleshooting skills in complex production ML and agentic AI environments. • Ability to lead complex technical initiatives without formal management authority. • Excellent communication skills to work effectively across engineering, product, and data science 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 and autonomous execution at scale. • Experience building ML systems in enterprise SaaS or large-scale product environments. • Experience supporting AI capabilities in regulated or enterprise domains. • 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 with accountability for the reliability of autonomous AI systems. • Strong problem solver who anticipates operational, product, and agentic failure modes. • Comfortable influencing technical and product strategy across teams without formal authority. • 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, ML systems, and product-driven AI workflows. • Calm and methodical when diagnosing complex ML, agentic, or production system issues.
Responsibilities
• Architect and lead the development of scalable ML platforms that support autonomous, goal-driven AI agents. • Design systems that support the full ML lifecycle, including agentic decision-making, task orchestration, and automated goal execution. • Build frameworks for integrating models with product logic, business objectives, and operational workflows. • Lead the development of pipelines that enable experimentation, productionization, and continuous agentic learning. • Define architecture standards and engineering practices for agentic AI, goal alignment, and productized ML solutions. • Collaborate with data science and product teams to turn research outputs into production AI agents that drive real product impact. • Design infrastructure supporting large-scale training, inference, and multi-agent coordination workloads. • Strengthen observability and monitoring across pipelines, AI agents, and goal-driven behavior execution. • Implement systems for automated evaluation, goal alignment checks, drift detection, and retraining. • Improve reliability, scalability, and operational excellence of ML services powering autonomous workflows. • Lead troubleshooting of complex agentic system failures and distributed ML infrastructure issues. • Influence CI/CD and development workflows supporting ML lifecycle, agent orchestration, and automated deployment. • Mentor engineers to build expertise in agentic systems, AI-driven product logic, and autonomous workflows. • Collaborate with architects and senior engineers to shape long-term AI platform strategy and agentic product roadmaps.
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