What are the responsibilities and job description for the AI/ML solution Architect position at StarTechs Inc.?
- Solution Architecture: Design and deploy end-to-end ML/AI architectures that integrate directly with client’s warehouse execution platform to solve complex logistics problems (e.g., wave management, task interleaving, and labor balancing).
- Master’s in Data Science, Computer Science, Industrial Engineering, Operations Research, etc.
- 7 years in applied data science with production AI and ML
- Strong Python (pandas, Pytorch/ TensorFlow, scikit-learn), SQL, experience with experimentation and statistical inference.
- Ability to work with event-driven data (timestamps, state transitions, logs).
- Demonstrated experience with production classification, forecasting, and anomaly detection algorithms (e.g., XGBoost, Random Forest, ARIMA/Prophet, LSTM, Isolation Forest) - not just familiarity with LLM-based tools.
- Familiarity with operational data sources including PLC/SCADA systems, historian databases, WES/WMS/WCS event logs, and sensor streams as inputs to ML pipelines.
- Use-case-first mindset: demonstrated ability to define a specific prediction target and identify required data before building infrastructure. Candidates who default to “build the platform first” are not the right fit for this role.
Familiarity with LLM orchestration, prompt engineering, and RAG (Retrieval-Augmented Generation) for operational intelligence use cases is a plus; primary focus of this role is operational ML, not generative AI.
- Experience with warehouse/fulfillment systems: WES/WMS/TMS, automation, labor management.
- Azure/Databricks experience: Databricks ML, Delta Lake, MLflow, feature engineering at scale.
- Experience deploying models into product workflows (API scoring, batch scoring, streaming signals).
- Strong background in Operations Research (OR), Linear Programming, or Reinforcement Learning
Salary : $80 - $85