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.

• Technical Expertise

• Strong proficiency in Python and ML libraries such as PyTorch, Tensor Flow, 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 (Torch Script, 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

• Experience optimizing deep learning models for NVIDIA Jetson–based edge systems.
• Experience contributing to platform‑agnostic AI/ML solutions.
• P…