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The AI/ML Engineer is responsible for designing, developing, and implementing artificial intelligence and machine learning models to solve enterprise IT and operational challenges. As an AI Solutions Owner, you will drive the roadmap for AI-powered automation within IT and networking environments. You will architect scalable solutions, including automated network diagnostics and AI agents for NOC operations, while leading the integration of LLMs. Managing the Agile backlog, you'll collaborate with engineering teams to build machine learning models for anomaly detection and capacity planning. By leveraging PowerShell, Python, and AIOps expertise, you will transform complex IT operational data into robust automation frameworks that optimize network performance.
Responsibilities:
Required Skills & Competencies:
(i) IT Service Automation – Orchestration, Scripting, & Process Assessment.
(ii) AI Engineering – Developing and deploying AI Agents and cutting-edge GenAI & ML solutions to address complex business challenges.
Job ID: 147586805
Skills:
snowflake , Hadoop Ecosystem, Pyspark, Tensorflow, Nlp, Pytorch, Docker, Python, AWS, Machine Learning Algorithms, Sql, Big Data Technologies, Data Warehousing Concepts, Gcp, Spark, Apache Kafka, Databricks, Azure, Kubernetes, Airflow, ML pipelines, ETL ELT pipelines, Deep Learning frameworks, Generative AI concepts, Infrastructure automation, Scikit-learn, CI CD pipelines, MLOps frameworks
Skills:
Java, Sql, Tensorflow, Nosql, MLops, Pytorch, Docker, Kubernetes, Python, Scikit-learn, MLflow, R, KServe
Skills:
Java, Sql, Nosql, Tensorflow, Pytorch, MLops, Docker, Python, Kubernetes, Scikit-learn, MLflow, R, KServe
Skills:
Java, Scripting, Orchestration, PowerShell, Itil, Gcp, Pytorch, Docker, Shell Script, Azure, Kubernetes, Python, Llama series, IT Service Automation, process assessment, AI ML libraries and frameworks, AI Engineering, GPT-4, Large Language Models
Skills:
Data Warehousing, Data Architecture, AI ML, Tensorflow, Pytorch, Version Control Systems, Python, Cloud Computing, Machine Learning Algorithms, DevOps practices, SageMaker, data lakes, ETL processes, model evaluation techniques, Statistical Modeling
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