We are an engineering and IT staffing agency recruiting an AI/ML Software Engineer for one of our clients. Our client is an American provider of industrial automation and digital transformation solutions for manufacturers, utilities, and industrial enterprises worldwide. Their technology portfolio spans smart factory systems, real-time monitoring, intelligent process control, IoT-enabled edge devices, and advanced predictive maintenance platforms. Leveraging AI and machine learning, the company helps customers unlock insights, optimize operations, and automate complex decisions across high-value production environments.
With a growing need to integrate AI-driven intelligence into control systems and software platforms, the company is seeking an experienced AI/ML Software Engineer to drive development of next-generation analytics, embedded inference models, and intelligent automation tools.
Typical Duties and Responsibilities
We are seeking a Senior AI/ML Software Engineer to design, develop, and deploy robust machine learning solutions for industrial-scale automation systems. This role will involve working closely with software architects, data scientists, and domain experts to deliver AI-powered capabilities across cloud, edge, and on-premise infrastructure.
Key responsibilities include:
- Architect and implement AI/ML models for applications such as predictive maintenance, anomaly detection, sensor fusion, quality inspection, and process optimization
- Collaborate with controls engineers and data teams to integrate ML models with SCADA systems, PLC data streams, and IoT platforms
- Develop and deploy inference models to industrial edge devices using platforms like NVIDIA Jetson, AWS Greengrass, Azure IoT Edge, or Intel OpenVINO
- Use version-controlled ML pipelines and MLOps tools for reproducible model training, validation, deployment, and monitoring
- Build and maintain scalable data pipelines using Apache Spark, Kafka, or Databricks to support ML workloads
- Leverage deep learning frameworks such as TensorFlow, PyTorch, and ONNX for development and inference
- Optimize ML model performance for low-latency, real-time environments with tools such as TensorRT, TVM, or TorchScript
- Implement industrial AI standards including ISA-95, OPC UA, and ensure models comply with safety-critical performance metrics
- Collaborate with cybersecurity teams to ensure AI models and interfaces meet NIST and IEC-62443 standards for secure deployment
Document architectures, algorithms, and workflows clearly for internal teams and external audit/compliance use
Education
- Bachelor’s degree in Computer Science, Machine Learning, Electrical Engineering, or related technical field
- Master’s or PhD in Artificial Intelligence, Data Science, Robotics, or Applied Mathematics preferred
Required Skills and Experience
- 10+ years of experience in software engineering, with at least 5+ years focused on AI/ML model development and deployment
- Advanced programming skills in Python, with proficiency in C++, Java, or Rust for production-level integration
- Extensive experience with ML and deep learning frameworks such as:
- TensorFlow, PyTorch, Keras, scikit-learn, ONNX
- Experience developing MLOps pipelines using tools such as Kubeflow, MLflow, DVC, or SageMaker
- Familiarity with edge AI deployment and hardware acceleration for inference (NVIDIA CUDA, Coral Edge TPU, etc.)
- Experience working with industrial automation protocols, fieldbus systems, and sensor data ingestion
- Understanding of real-time systems, embedded firmware integration, and RTOS constraints
- Strong background in data preprocessing, feature engineering, model validation, and hyperparameter tuning
- Excellent communication skills and ability to collaborate across cross-disciplinary engineering and operations teams
Preferred Qualifications
- Certifications such as:
- Google Cloud Professional ML Engineer
- AWS Certified Machine Learning – Specialty
- Microsoft Certified: Azure AI Engineer Associate
- Certified AI Practitioner (CAIP)
- Experience in digital twin modeling, reinforcement learning, or time-series forecasting for industrial systems
- Background working with Manufacturing Execution Systems (MES) and ERP data integration
- Familiarity with robotic automation, machine vision, or autonomous control loops using AI
- Experience contributing to open-source ML libraries or participating in Kaggle competitions
