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Roles & Responsibilities
We're looking for an experienced AI Engineer to join our AI and Data Department, driving the development, deployment, and governance of cutting-edge AI, ML, and Generative AI (GenAI) solutions. In this role, you'll work closely with business and technology teams to design scalable, secure, and intelligent systems that accelerate digital transformation and enterprise AI adoption.
What You'll Do:
What You'll Bring:
We are a technology services company with an experienced team focused on delivering dynamic and flexible solutions to enhance our client’s ability to achieve their tactical and strategic business objectives.
Our ecosystem allows us to collaboratively innovate, disrupt and evolve with our partners and clients in the rapidly changing technology landscape.
We bring an experienced team, visionary leadership, strategic partnerships, regional reach with a uniquely defined service model to deliver best in class services for our clients.
Visit www.percept-solutions.com for more details
Job ID: 131090619
Skills:
Tensorflow, Pytorch, Keras, Python, software version control systems such as Git, data processing concepts, AI and machine learning frameworks
Skills:
preprocessing , pruning , Github, Predictive Analytics, Apis, Azure, AWS, feature stores, TensorRT, Training, CI CD, Monitoring, ML model versioning, ONNX, AI ML pipelines, AI MLOps, data ingestion, quantization, cloud-native services, Deployment
Skills:
snowflake , Java, Tensorflow, Pytorch, Python, Scikit-learn
Skills:
Machine Learning, Erp, Tensorflow, Pytorch, Predictive Analytics, AWS, Google Cloud, Deep Learning, Keras, Azure, Edge AI, IoT platforms, Plc, AI models, Historians, SCADA, Generative AI, LLMs, Prescriptive Analytics, Dcs, Condition-Based Maintenance, RAG, Multi-agent systems, Mes, Reliability Analytics
Skills:
Machine Learning, Tensorflow, Nlp, Pytorch, Python, AWS, Java, Hadoop, Artificial Intelligence, Data Structures, Big Data, Deep Learning, Algorithms, Gcp, MLops, Spark, Keras, Azure, Computer Vision, Explainability, R, Statistics, reinforcement learning, Model Interpretability
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