AI Engineering - Solutions Architect

Overview

Lead the architecture, development, deployment, and optimization of scalable AI and machine learning solutions for HPE Networking's Digital Experience & Automation team. The role applies AI, data engineering, automation, and analytics to customer case data, bug data, operational data, and other business performance metrics.

What you'll do

• Lead and develop scalable AI solutions using relevant AI, machine learning, deep learning, and generative AI techniques.
• Architect large-scale AI solutions that merge AI models and techniques into the software development life cycle.
• Organize and lead comprehensive code and design review sessions.
• Mentor and provide feedback to junior and mid-level team members.
• Research and stay up to date with advancements in AI and machine learning technologies, frameworks, and algorithms.
• Explore and experiment with techniques to solve complex problems and improve existing models.
• Collaborate with cross-functional teams to understand business requirements and design AI and machine learning solutions.
• Determine appropriate algorithms, models, and frameworks and architect systems for scalability, efficiency, and robustness.
• 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 key business performance metrics.
• Build infrastructure for extraction, transformation, and loading of data from a wide variety of data sources.
• Work with data and analytics specialists to improve functionality in data systems.
• Identify trends and patterns in datasets to scope opportunities for automation.
• Deploy machine learning models into production environments, considering scalability, performance, and security.
• Integrate models with existing software systems and infrastructure.
• Monitor deployed model performance, collect relevant metrics, and analyze data to identify areas for improvement.
• Fine-tune models, optimize algorithms, and enhance system performance based on monitoring and analysis.
• Work with the engineering manager and team lead to set design and implementation standards.
• Lead meetings and 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 and provide insights and recommendations.
• Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from datasets.

What you'll need

• 11+ years of experience in a data science role.
• A graduate degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field.
• 6+ years of experience building data pipelines for data science-driven solutions and deploying multiple solutions in a production environment.
• In-depth knowledge in one or more AI technologies: Classical ML, NLP, Gen AI, Agentic AI, or 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.
• Good team-working skills with excellent interpersonal, written, verbal, and presentation skills.
• Experience building and optimizing data pipelines, architectures, and datasets.
• Experience performing root cause analysis on internal and external data and processes to answer specific business questions and find opportunities for improvement.
• Strong analytical skills related to working with unstructured datasets.
• Experience building processes supporting data transformation, data structures, metadata, dependency, and workload management.
• A successful history of manipulating, processing, and extracting value from large, disconnected datasets.
• Experience identifying, designing, and implementing internal process improvements, including automating manual processes, optimizing data delivery, and redesigning infrastructure for greater 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.
• In-depth knowledge of linear algebra, calculus, probability theory, and statistical concepts.
• Understanding of and ability to develop complex machine learning models and algorithms.
• Good knowledge of Software Development Life Cycle 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, ScikitLearn, MLlib, TensorFlow, and NLP libraries.
• Experience working with Databricks and Snowflake platforms.
• Experience with AWS, S3, Spark, Kafka, and Elastic Search.
• Experience with big data tools such as Hadoop, Spark, and Kafka.
• Experience with relational SQL and NoSQL databases, including Postgres 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.

Details

• Location: Bengaluru, Karnataka, India.
• Onsite role with an expectation that you will primarily work from an HPE office.