Description and Requirements
At Lenovo, we are driven by a bold vision to deliver Smarter Technology for All. Our mission is to develop transformative technologies that foster a more inclusive, trustworthy, and sustainable digital society. Through the design and engineering of the world's most comprehensive portfolio of smart devices and infrastructure, we are also leading innovation in developing and delivering Hybrid AI solutions that reinvent the way users interact with their fleet of devices. Our Hybrid AI Platform allows for development of AI based solutions that are deployed on Cloud, Agentic AI solutions and Edge solutions on millions of devices. It also gathers data that is analyzed and processed to provide intelligent and innovative solutions with cutting-edge technologies. Join us in shaping the future of intelligent device management and defining the next generation of smarter technology.
About this role
Lenovo Cloud and Software team is seeking an experienced AI and Machine Learning Engineer to develop traditional machine learning and large language models in a fast paced environment. This role is also expected to be proficient in data processing techniques, transformation, develop data pipelines to enable consumption by the programmed models. To be successful in this role, candidates must be hands-on developer with rich knowledge of data lake architecture, machine learning and AI concepts using Python and its library frameworks. This role requires extensive coordination with multiple teams and stakeholders throughout the lifecycle of development. Close collaboration with Architecture team, Product Lifecycle management team, product assurance team, security and others. Release management through Agile processes is a primary responsibility for this role. A successful candidate in this role would have well rounded knowledge of traditional Machine Learning, Data, Cloud and AI concepts. Additionally they should have experience in designing and tuning application-level performance and scalability, while designing with security-first principles.
Primary Skills and Responsibilities
- At least 4 years of relevant experience of full-time working experience in Machine Learning, MLOps
- Hands on Experience with MLOps methods and platforms such as MLFlow
- Strong Data Engineering knowledge with technologies such as Apache AirFlow, PySpark
- Experience designing, training and validating ML models
- Experience with testing technologies for various ML Models
- Experience in designing and developing Unsupervised models for clustering, pattern analysis and anomaly detection, forecasting.
- Experience in handling Time Series Data, Anomalies detection, Data Correlation techniques
- Well versed with model optimization, resources handling, recall and precision tuning, improving performance while maintaining inference quality.
- Expert at big data processing, transformations, optimized pipeline processing, Lakehouse architecture.
- Hands-on development of Solutions in Data Engineering technologies & AI
- Participating in daily working sessions, and leading workstreams from planning through execution to closure
- Hands on development experience in Python, using related libraries such as Pandas, Pytorch, TensorFlow, Spark
- Good understanding of SaaS platforms and associated technologies
- Proficiency in Containeration, Orchestration of containers, scaling concepts
- Expertise in AWS Bedrock, SageMaker
- Thorough understanding of Agile Development practices, ceremonies
- Excellent communication skills
Preferred Skills
- Certifications in AWS or equivalent Cloud technologies and AI, ML paths
- Microservices architecture knowledge and REST based solutions
- Excellent communication, interpersonal, and presentation skills, including clear descriptions and document generation, such as class diagrams, sequence diagrams, and protocol definitions.
- Data Science experience with R
- Client side technologies and experience with Edge AI, TinyML, TensorFlow Lite
- Strong mathematical knowledge in Statistics, Calculus, Linear Algebra
Technologies
- Proficiency in programming languages such as Python, Java, C++, TypeScript based languages
- Expertise in Machine Learning frameworks such as PyTorch, LangChain, LangGraph, R
- Cloud technologies/services (AWS, Azure)
- Big Data tools (Hadoop, Spark, Flume, Kafka)
- Messaging Protocols such as Kafka, MQTT
- Data Pipeline and Workflow management tools (Airflow, MLFlow)
- SQL and NoSQL Databases
- Stream processing, Java, Python
- Spring framework is a plus
- Knowledge of multiple Operating Systems such as Windows, Macintosh, Linux, Android, iOS
- Algorithmic knowledge of various models and their applicability
- SourceControl systems such as Git, BitBucket
- Atlassian tools familiarity - JIRA, Confluence, BitBucket
