01Key Responsibilities
- Develop and optimize enterprise-wide search systems and models.
- Design algorithms to improve indexing, query relevance, and search accuracy.
- Support taxonomy, ontology, and metadata model creation for better search outcomes.
- Collaborate with business units to build AI-enabled search features.
- Analyze user behavior and system metrics to refine search performance.
- Work with engineers, PMs, and designers to deliver integrated search solutions.
- Develop production-grade ML systems for ranking, personalization, and recommendations.
- Participate in proof-of-concept initiatives with internal and external partners.
- Follow best practices in CI/CD, testing, and monitoring.
- Stay current on emerging AI/ML developments and apply them practically.
02Requirements
- BTech/MTech/BE/MSc in Computer Science, Mathematics, Statistics, or related field.
- 58 years in Search, Information Retrieval, NLP, and Machine Learning.
- Hands-on experience deploying ML/AI systems at scale.
- Mentoring experience preferred.
- Strong knowledge of search technologies (indexing, faceted search, NLP-based search).
- Programming skills in Python, Java, or Scala.
- Familiarity with TensorFlow, PyTorch, scikit-learn, Keras.
- Experience with SQL/NoSQL databases (Cosmos DB, MongoDB, Cassandra).
- Exposure to CI/CD pipelines, Git, Jenkins, and testing frameworks.
- Good understanding of data structures, algorithms, OOP concepts, and Linux.
Good to Have:
- Experience with Elasticsearch/Solr or Lucene-based engines.
- Knowledge of distributed systems and containers (Docker/Kubernetes).
- Understanding of cloud-based ML deployments (Azure/AWS/GCP).
- Exposure to personalization/recommendation systems.
- Research exposure or AI/ML certifications. .
