2 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Lead and develop scalable AI solutions using machine learning, deep learning, generative AI, and related techniques.
- Architect large-scale AI solutions that integrate models into the software development lifecycle.
- Build, optimize, and manage data pipelines, data architectures, datasets, and ETL infrastructure.
- Develop, train, deploy, monitor, and continuously improve machine learning models in production.
- Build analytics tools that extract insights from customer case, bug, operational, and business performance data.
- Apply data mining, data modeling, NLP, and machine learning to analyze structured and unstructured datasets.
- Identify automation opportunities and implement scalable internal process and infrastructure improvements.
- Lead code and design reviews, establish implementation standards, mentor junior and mid-level team members, and provide technical leadership.
- Collaborate with engineering managers, team leads, data specialists, and cross-functional stakeholders.
- Deliver strategic technical presentations and reports with recommendations to senior stakeholders.
Requirements
- 11+ years of experience in a data science role.
- Graduate degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field.
- 6+ years of experience building data pipelines for data science solutions and deploying multiple solutions in production.
- In-depth knowledge of one or more of classical machine learning, NLP, generative AI, agentic AI, or MCP.
- Experience with technical support datasets, including CRM data, hardware and software bug data, and machine logs.
- Experience building and optimizing data pipelines, architectures, and datasets, including data transformation, metadata, dependencies, and workload management.
- Strong hands-on coding skills, preferably in Python, for large-scale data processing and machine learning model development.
- Strong mathematics and statistics foundation, including linear algebra, calculus, probability theory, and statistical concepts.
- Experience with large language models, generative AI, and conversational AI.
- Experience with NumPy, Scikit-learn, MLlib, TensorFlow, or NLP libraries.
- Experience with Databricks, Snowflake, AWS, S3, Spark, Kafka, Elasticsearch, Hadoop, relational SQL and NoSQL databases, PostgreSQL, and Cassandra.
- Experience with AWS cloud services including EC2, EMR, RDS, and Redshift, plus stream-processing systems such as Storm and Spark Streaming.
- Knowledge of software development lifecycle practices and Agile principles.
- Strong analytical, communication, presentation, collaboration, root-cause analysis, and stakeholder-management skills.
Benefits
- Health and wellbeing benefits supporting physical, financial, and emotional wellbeing.
- Personal and professional development programs supporting career growth and movement across divisions.
- Inclusive workplace with accessibility support and reasonable-accommodation options.
- Onsite work arrangement with the expectation of primarily working from an HPE office.
Tech Stack
Amazon RedshiftApache CassandraApache HadoopApache KafkaApache SparkApache StormAWSDatabricksElasticsearchNumPyPostgreSQLPythonscikit-learnSnowflakeSQLTensorFlow
Categories
Data EngineeringML Engineering
About HPE
Hewlett Packard Enterprise (HPE) builds enterprise IT infrastructure and services, including servers, storage, networking (Aruba), and hybrid edge-to-cloud platforms like HPE GreenLake for businesses and public-sector organizations. Formed in 2015 after Hewlett-Packard’s split, HPE is headquartered in Houston, Texas and trades on the NYSE. It sells hardware, software, and subscription-based managed cloud services to help customers run workloads across data centers, colocation, and public clouds.