The RoleYou'll build and deploy the intelligent systems at the core of Merciv. Our platform doesn't just surface insights - it reasons, forecasts, and acts autonomously across complex enterprise data landscapes. You'll develop the models and agentic architectures that power demand forecasting, consumer intelligence, competitive analysis, and autonomous decision-making for the world's largest retailers.
This is applied AI at its most impactful. You'll work at the intersection of cutting-edge research and real enterprise deployment, building systems that generate tens of millions in value for clients.
What You'll Do- Design, build, and deploy ML models for demand forecasting, time series prediction, consumer sentiment analysis, and anomaly detection at enterprise scale
- Develop and iterate on Merciv's agentic AI architecture - building systems that reason across heterogeneous data sources and take autonomous action
- Build and maintain robust ML pipelines: data preprocessing, feature engineering, model training, evaluation, and production deployment
- Architect RAG systems and LLM integrations that power Merciv's natural language interfaces and autonomous workflows
- Collaborate with backend engineers to ensure models are production-grade - optimized for latency, reliability, and scale
- Own model performance end-to-end: monitoring, retraining, and continuous improvement in production
- Stay at the frontier of AI research and bring relevant innovations into the platform
- You have 5+ years of applied ML/AI experience with models deployed in production
- You have an M.S. or Ph.D. in CS, Machine Learning, Statistics, or equivalent practical experience
- You're deeply proficient in Python with hands-on experience in PyTorch, TensorFlow, or scikit-learn
- You have a strong background in statistical analysis, predictive modeling, and time series forecasting
- You've worked on agentic AI systems, multi-agent orchestration, NLP, LLMs, and RAG architectures
- You care about model interpretability and building systems enterprise users can actually trust
- Former technical founders are encouraged to apply - we care more about what you've built than how long you've been building
