AI- ML Architect

Title:

Position Summary:

AI/ML Architect

Key Responsibilities:

Architecture & Strategy

• Define the end-to-end architecture for AI/ML systems including

  time series forecasting

  ,

  computer vision

  , and

  real-time classification

  .
• Design scalable ML pipelines (training, validation, deployment, retraining) using MLOps best practices.
• Architect hybrid deployment models supporting both

  cloud

  and

  edge inference

  for low-latency processing.

Model Integration

• Guide the integration of ML models into the IIoT platform for real-time insights, alerting, and decision support.
• Support model fusion strategies combining disparate data sources, sensor streams with visual data (e.g., object detection + telemetry + 3rd party data ingestion).

MLOps & Engineering

• Define and implement ML lifecycle tooling, including version control, CI/CD, experiment tracking, and drift detection.
• Ensure compliance, security, and auditability of deployed ML models.

Collaboration & Leadership

• Collaborate with Data Scientists, ML Engineers, DevOps, Platform, and Product teams to align AI efforts with business goals.
• Mentor engineering and data teams in AI system design, optimization, and deployment strategies.
• Stay ahead of AI research and industrial best practices; evaluate and recommend emerging technologies (e.g., LLMs, vision transformers, foundation models).

Must-Have Qualifications:

• Bachelor’s or Master’s degree in Computer Science, AI/ML, Engineering, or a related technical field.

• 8+ years of experience

  in AI/ML development, with

  3+ years

  in architecting AI solutions at scale.
• Deep understanding of ML frameworks (TensorFlow, PyTorch), time series modeling, and computer vision.
• Proven experience with

  object detection, facial recognition, intrusion detection

  , and

  anomaly detection

  in video or sensor environments.
• Experience in

  MLOps

  (MLflow, TFX, Kubeflow, SageMaker, etc.) and

  model deployment on Kubernetes/Docker

  .
• Proficiency in edge AI (Jetson, Coral TPU, OpenVINO) and cloud platforms (AWS, Azure, GCP).

Nice-to-Have Skills:

• Knowledge of stream processing (Kafka, Spark Streaming, Flink).
• Familiarity with OT systems and IIoT protocols (MQTT, OPC-UA).
• Understanding of regulatory and safety compliance in AI/vision for industrial settings.
• Experience with charts, dashboards, and integrating AI with front-end systems (e.g., alerts, maps, command center UIs).

Role Impact:

shape the intelligence layer