Lenovo

Responsibilities:
1.    Design, develop, and optimize search algorithms to improve the accuracy, relevance, and speed of search result at both index time (data ingestion) and query time (search response)
2.    Implement machine learning techniques, including natural language processing (NLP), deep learning and large language models to enhance search functionality.
3.    Analyze large datasets to identify patterns, trends, and insights that inform search algorithm enhancements.
4.    Collaborate with product managers, data scientists, and other stakeholders to understand requirements and prioritize initiatives.
5.    Conduct A/B testing and performance monitoring to evaluate the effectiveness of search algorithms and iterate on improvements.
6.    Stay updated on emerging trends and advancements in search technology and machine learning to drive innovation and maintain a competitive edge. 
7.    Best practices developer – you write code that is well-tested, well-documented, has a clean API, and can be re-used.
Requirements:  
1.    Bachelor's degree in computer science, engineering, or a related field; advanced degree preferred.
2.    Minimum of 5 years of experience in software engineering, with a focus on search technology and machine learning.
3.    Strong proficiency in programming languages such as Python, Java, or C++
a.    Networking, threading, locks and caching
4.    Should be proficient with 
a.    Scala, Spark, Docker, ML, Algorithms
b.    Data Science tools: Python (Pandas, NumPy, SciPy, scikit-learn), Jupyter.
c.    Web based services
5.    Experience in cloud environment.  Have experience with Kubernetes, Azure
6.    Demonstrated expertise in machine learning techniques and frameworks, such as TensorFlow, PyTorch, or scikit-learn.
7.    Experience with search platforms and technologies, such as Elasticsearch, Solr, or Lucene.
8.    Solid understanding of data structures, algorithms, and information retrieval principles (text mining and indexing).
9.    Well versed in SOLR Documents, Filters, Query Parsers and their customization. Should have in-depth knowledge of Schema development, Dynamic Fields, Performance Tuning, Faceted Search, Grouping, Distributed Searches, Lucene internals.
10.    SOLR Search architect Hands on experience in SOLR implementations Design, Architecture, Implementation, Performance, Scalability of Apache SOLR
11.    Familiarity with other document-store technologies.
12.    Excellent problem-solving skills and the ability to thrive in a fast-paced, collaborative environment.
13.    Strong communication skills and the ability to effectively convey complex concepts to technical and non-technical stakeholders.
Skills:
1.    Platform Performance (Monitoring, Tuning)
2.    Content gathering and Index management
3.    Manage/Develop process for bulk and rapid indexing
4.    Manage/Develop content gathering methods (connectors, crawlers)
5.    Content processing pipelines
6.    Query processing pipelines
7.    Geo/country/store Profile management
8.    Designing queries for stores
9.    Designing ranking models for stores
10.    Linguistics management
11.    Domain dictionaries
12.    Synonyms for various languages, domain dependent terms
13.    Personalization
14.    Signal processing pipelines
15.    Develop personalization models
16.    Search Analysis
17.    Observe search trends
18.    Help Geo’s identify the issues and address them
19.    Manage rules engine along with Geo’s