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Hewlett Packard Enterprise

Builds servers, networking and private cloud

AI Engineering Solutions Architect

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listed

About the Job

Requirements

  • At least 11 years of experience in a data science role and a graduate degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field.
  • At least 6 years of experience building data pipelines for data science-driven solutions and deploying multiple solutions in production.
  • In-depth knowledge of one or more AI technologies, such as classical machine learning, natural language processing, generative AI, agentic AI, and MCP.
  • Experience working in a technical support environment with datasets from CRM, hardware and software bug data, and machine logs.
  • Experience supporting and working with multifunctional teams in a multidimensional, fast-paced environment.
  • Experience building and optimizing data pipelines, architectures, and datasets.
  • Experience performing root cause analysis on internal and external data and processes to answer business questions and identify improvement opportunities.
  • Strong analytic skills for working with unstructured datasets.
  • Ability to build processes supporting data transformation, data structures, metadata, dependency management, and workload management.
  • A successful history of manipulating, processing, and extracting value from large, disconnected datasets.
  • Ability to identify, design, and implement internal process improvements, including automating manual processes, optimizing data delivery, and redesigning infrastructure for scalability.
  • Strong hands-on coding skills, preferably in Python, for processing large-scale datasets and developing machine learning models.
  • A strong foundation in mathematics and statistics, including linear algebra, calculus, probability theory, and statistical concepts, and understanding of complex machine learning models and algorithms.
  • Knowledge of the software development lifecycle and Agile principles.
  • Experience working with large language models, generative AI, and conversational AI.
  • Familiarity with one or more machine learning or statistical modeling tools, such as NumPy, scikit-learn, MLlib, TensorFlow, or natural language processing libraries.
  • Experience with Databricks and Snowflake platforms.
  • Experience with AWS, S3, Spark, Kafka, and Elasticsearch.
  • Experience with big data tools such as Hadoop, Spark, and Kafka.
  • Experience with relational SQL and NoSQL databases, including PostgreSQL and Cassandra.
  • Experience with data pipeline and workflow management tools.
  • Experience with AWS cloud services, including EC2, EMR, RDS, and Redshift.
  • Experience with stream-processing systems such as Storm and Spark Streaming.

Responsibilities

  • Lead and develop scalable AI solutions using relevant artificial intelligence, machine learning, deep learning, and generative AI techniques.
  • Architect large-scale AI solutions that integrate AI models and techniques into the software development lifecycle.
  • Organize and lead comprehensive code and design review sessions, and guide discussions to align with project requirements and best practices.
  • Research and stay current with advancements in AI and machine learning technologies, frameworks, and algorithms; explore techniques to solve problems and improve models.
  • Collaborate with cross-functional teams to understand business requirements, select appropriate algorithms, models, and frameworks, and architect scalable, efficient, and robust systems.
  • Develop, implement, and optimize machine learning models and algorithms, including data preprocessing, feature engineering, model selection, hyperparameter tuning, and training on large datasets; monitor and improve model performance and accuracy.
  • Leverage or build analytics tools using data pipelines to provide insights into customer case data, bug data, operational data, and other business performance metrics.
  • Build infrastructure to extract, transform, and load data from a wide variety of sources.
  • Work with data and analytics specialists to improve the functionality of data systems.
  • Identify trends and patterns in datasets to scope automation opportunities.
  • Deploy machine learning models into production environments with attention to scalability, performance, and security.
  • Integrate models with existing software systems and infrastructure to ensure smooth operation and interoperability.
  • Monitor deployed models, collect metrics, analyze opportunities for improvement, and use findings to fine-tune models, optimize algorithms, and improve system performance.
  • Work with the engineering manager and team lead to set design and implementation standards and support continuous improvement and alignment with project goals.
  • Lead meetings to foster a collaborative and productive team environment.
  • Provide technical leadership, mentorship, and guidance to junior team members.
  • Address and resolve challenges proactively.
  • Develop and deliver strategic presentations and reports to senior stakeholders, providing technical and business insights and recommendations.
  • Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from datasets.

About the Company

Hewlett Packard Enterprise

Builds servers, networking and private cloud

Hewlett Packard Enterprise sells servers, storage, networking and hybrid cloud software to large companies, governments and service providers. Its GreenLake platform lets customers run on-premises hardware as a metered cloud service, and its Cray supercomputers run national lab and AI training workloads. The networking business, built on Aruba and Juniper Networks, supplies campus, data-center and routing equipment. Revenue comes from hardware sales, multiyear support contracts, financing and subscriptions. The company traces its roots to Hewlett-Packard, founded in 1939, became a separate public company in 2015 and is headquartered in Houston, Texas.

Company size
10,001+
Company stage
IPO
Headquarters
Houston, TX
Founded
1939

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