Sr AI Engineer for Next-Gen Analytics
Budget: $8 – $15 USD
I am looking for a Senior AI Engineer to join a stealth-mode project focused on next-gen anomaly detection and predictive analytics for high-frequency sensor data.
We are moving beyond traditional deep learning (LSTMs/Autoencoders) to implement Time-Series Foundation Models and Agentic Workflows that handle end-to-end diagnostics on the Edge and Cloud.
What We Are Building
Modern Time-Series Modeling: Implementing SOTA architectures (Transformers, Mamba, Foundation Models) for anomaly detection and forecasting.
Agentic RCA: Building LLM-based Agents (LangGraph/LangChain) to interpret anomalies, perform Root Cause Analysis (RCA), and query technical knowledge bases (RAG).
Edge Deployment: Optimizing complex models for low-latency inference on edge devices.
Required Skills
Advanced Time-Series: Deep knowledge of modern architectures beyond LSTMs. Experience with Transformers (PatchTST, iTransformer) or Foundation Models (TimesFM, Chronos, Lag-Llama).
GenAI & Agents: Experience building Agentic systems that interact with structured data and vector stores (RAG).
Engineering & MLOps: Strong proficiency in PyTorch and PySpark/Databricks for large-scale telemetry processing.
Edge AI: Experience with model optimization (ONNX, TensorRT, Quantization) for resource-constrained environments.
We are moving beyond traditional deep learning (LSTMs/Autoencoders) to implement Time-Series Foundation Models and Agentic Workflows that handle end-to-end diagnostics on the Edge and Cloud.
What We Are Building
Modern Time-Series Modeling: Implementing SOTA architectures (Transformers, Mamba, Foundation Models) for anomaly detection and forecasting.
Agentic RCA: Building LLM-based Agents (LangGraph/LangChain) to interpret anomalies, perform Root Cause Analysis (RCA), and query technical knowledge bases (RAG).
Edge Deployment: Optimizing complex models for low-latency inference on edge devices.
Required Skills
Advanced Time-Series: Deep knowledge of modern architectures beyond LSTMs. Experience with Transformers (PatchTST, iTransformer) or Foundation Models (TimesFM, Chronos, Lag-Llama).
GenAI & Agents: Experience building Agentic systems that interact with structured data and vector stores (RAG).
Engineering & MLOps: Strong proficiency in PyTorch and PySpark/Databricks for large-scale telemetry processing.
Edge AI: Experience with model optimization (ONNX, TensorRT, Quantization) for resource-constrained environments.