Sr Advanced AI Engineer

As a Senior Advanced AI Engineer, you will design, develop, and deploy AI-driven solutions for smart buildings and industrial automation systems. Your primary focus will be building advanced ML models, integrating them into real-world control environments, and driving innovation across HVAC, lighting, security, and energy optimization. You will collaborate cross‑functionally, mentor junior engineers, and influence multiple projects with your technical expertise.

• AI Solutions Design & Integration
  • Design and integrate AI/ML models into Building Management Systems (BMS) and Industrial Control Systems (ICS), including SCADA and PLC environments.
  • Implement real‑time API–based and batch‑inference workflows.
  • Develop model feedback loops to support continuous learning and performance improvement.
  • Build algorithms for real‑time decision‑making using sensor, IoT, and industrial process data. 
• Data Engineering
  • Partner with Data Engineering teams on ETL workflows and data preparation for large‑scale building and industrial datasets (e.g., HVAC telemetry, energy consumption, machine performance).
  • Contribute to feature engineering and ensure data readiness for modeling
  • Support the development of training pipelines that leverage model registries and tracking systems. 
• Innovation & Research
  • Explore emerging technologies such as generative AI, digital twins, multimodal foundation models, and autonomous control systems.
  • Lead proof‑of‑concept initiatives and mentor junior engineers through early‑stage experimentation.
  • Translate innovative concepts into practical solutions for automation and building intelligence. 
• Performance Optimization
  • Collaborate with MLOps teams to optimize real-time inference across platforms (AKS, GKE, on‑prem microk8s).
  • Work with production‑ready inference runtimes such as vLLM, ONNX Runtime, and NVIDIA Triton.
  • Contribute to model conversion, quantization, and optimization for efficient inference.
  • Partner with platform engineers on deployment strategies, scalability, and monitoring.
• Compliance & Security
  • Ensure all AI solutions comply with cybersecurity standards and industrial safety protocols.
  • Maintain training and inference repositories to meet corporate and industry security requirements.

MUST HAVE

• Technical Expertise
  • Strong proficiency in Python and ML libraries such as PyTorch, TensorFlow, JAX, XGBoost, and scikit‑learn.
  • Experience with Kubernetes, Databricks, or comparable platforms. 
  • Familiarity with CI/CD practices for AI/ML workflows. 
  • Working knowledge of PySpark for data exploration and pipeline contributions.
  • Strong debugging, profiling, and performance engineering skills in Python. 
• AI/ML Knowledge
  • Expertise in one or more key domains: NLP, time-series forecasting, computer vision, or reinforcement learning.
  • Ability to build models with noisy or sparsely labeled datasets.
  • Experience using MLflow or similar tools for tracking, reproducibility, and model registry.
  • Knowledge of converting models for production inference (TorchScript, ONNX).
  • Experience with model performance optimization (e.g., quantization, latency tuning).
  • Working knowledge of applying, fine‑tuning, and optimizing foundation models for domain-specific tasks across text, vision, or time‑series modalities. 
  • Ability to make informed accuracy–cost trade-offs during model design. 
• Innovation Skills
  • Ability to identify emerging AI trends and translate them into practical solutions.
  • Experience in rapid prototyping, proof‑of‑concept development, and technology scouting.
  • Strong problem‑solving mindset with a focus on creative and disruptive solutions. 
• Cloud & Edge Computing
  • Knowledge of AI/ML offerings from major cloud providers (Azure, GCP, or AWS).
  • Experience deploying AI/ML solutions on edge devices (e.g., NVIDIA Jetson) is a plus but not mandatory.
• Education & Experience
  • Bachelor’s degree in Computer Science, Electrical Engineering, or a related field; Master’s degree preferred.
  • Bachelor’s + 6 years of relevant AI/ML experience
  • Master’s + 4 years of relevant AI/ML experience
  • PhD + 2 years of relevant AI/ML experience

WE VALUE

• Experience optimizing deep learning models for NVIDIA Jetson–based edge systems.
• Experience contributing to platform‑agnostic AI/ML solutions.
• Proven end‑to‑end ownership of the ML lifecycle, including training, deployment, and feedback loops.
• Experience with smart building platforms, SCADA systems, or energy management solutions.