AI Engineering - Solutions Architect
Hewlett Packard Enterprise (HPE) (company site, opens in a new tab)11+ yrsBengaluruOn-siteDirect career pageEst. 40–75 LPA
Posted on 28 Sept
Reach people at Hewlett Packard Enterprise (HPE)
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Hewlett Packard Enterprise (HPE) you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead design, development, and deployment of scalable AI/ML solutions integrating AI techniques into the software development lifecycle.
- Architect large-scale AI systems ensuring scalability, efficiency, and robustness while overseeing model performance and continuous improvement.
- Provide technical leadership including code/design reviews, mentoring junior team members, and delivering strategic reports to senior stakeholders.
Minimum Requirements
- 11+ years of experience in data science roles with Graduate degree in Computer Science, Statistics, Informatics, Information Systems or related quantitative field.
- 6+ years experience building and deploying multiple data pipeline and AI solutions into production environments.
- In-depth expertise in Classical ML, NLP, Generative AI, Agentic AI, and experience with datasets from CRM, hardware/software bugs, machine logs.
- Mandated onsite work from HPE office; strong programming skills in Python and experience with AWS, Spark, Kafka, Databricks, Snowflake, and big data tools.
Ideal Candidate Profile
- Experienced technical leader capable of managing complex AI engineering projects in fast-paced, multidimensional environments with cross-functional teams.
- Strong background in deploying AI at scale with hands-on expertise in machine learning model lifecycle and data pipeline architecture.
- Demonstrated ability to translate business needs into AI/ML solutions and leverage data insights for impactful automation and operational improvements.