- Architect and lead the development of scalable, intelligent search systems leveraging NLP, embeddings, LLMs, and vector search
- Own the end-to-end lifecycle of search solutions, from ingestion and indexing to ranking, relevancy tuning, and UI integration
- Build systems that surface scientific literature, clinical trial data, regulatory content, and real-world evidence using semantic and contextual search
- Integrate AI models that improve search precision, query understanding, and result summarization (e.g., generative answers via LLMs).
- Partner with platform teams to deploy search solutions on scalable infrastructure (e.g., Kubernetes, cloud-native services, Databricks, Snowflake).
- Experience in Generative AI on Search Engines
- Experience in integrating Generative AI capabilities and Vision Models to enrich content quality and user engagement.
- Building and owning the next generation of content knowledge platforms and other algorithms/systems that create high quality and unique experiences. Designing and implementing advanced AI Models for entity matching, data duplication.
- Experience Generative AI tasks such as content summarization. deduping and metadata quality. Researching and developing advanced AI algorithms, including Vision Models for visual content analysis.
- Implementing KPI measurement frameworks to evaluate the quality and performance of delivered models, including those utilizing Generative AI.
- Developing and maintaining Deep Learning models for data quality checks, visual similarity scoring, and content tagging.
- Continually researching current and emerging technologies and proposing changes where needed.
- Implement GenAI solutions, utilize ML infrastructure, and contribute to data preparation, optimization, and performance enhancements.
- Manage and mentor a cross-functional engineering team focused on AI, ML, and search infrastructure.
- Foster a collaborative, high-performance engineering culture with a focus on innovation and delivery.
- Work with domain experts, data stewards, oncologists, and product managers to align search capabilities with business and scientific need
- Degree in computer science & engineering preferred with 9-12 years of software development experience
- Proficient in Spark, Kafka, Snowflake, Delta Lake, Hadoop, databricks, Mongo DB, S3 Buckets, ELT& ETL, API Integrations, Java
- Proven experience building search systems with technologies like Elasticsearch, Solr, OpenSearch, or vector DBs (e.g., Pinecone, FAISS).
- Hands-on experience with various AI models, GCP Search Engines, GCP Cloud services
- Strong understanding of NLP, embeddings, transformers, and LLM-based search applications
- Proficient in programming language AI/ML, Python, GraphQL, Java Crawlers, Java Script, SQL/NoSQL, Databricks/RDS, Data engineering, S3 Buckets, dynamo DB
- Strong problem solving, analytical skills; Ability to learn quickly; Excellent communication and interpersonal skills
- Experience deploying ML services and search infrastructure in cloud environments (AWS, Azure, or GCP)
- Experience in AI/ML, Java, Rest API, Python
- React, GraphQL, NLMS, Full stack applications, Solr Search
- Experienced with Fast Pythons API
- Experience with design patterns, data structures, data modelling, data algorithms
- Knowledge of ontologies and taxonomies such as MeSH, SNOMED CT, UMLS, or MedDRA.
- Familiarity with MLOps, CI/CD for ML, and monitoring of AI models in production.
- Experienced with AWS /Azure Platform, building and deploying the code
- Experience in PostgreSQL /Mongo DB SQL database, vector database for large language models, Databricks or RDS, S3 Buckets
- Experience in Google cloud Search and Google cloud Storage
- Experience with popular large language models
- Experience with LangChain or LlamaIndex framework for language models
- Experience with prompt engineering, model fine tuning
- Knowledge of NLP techniques for text analysis and sentiment analysis
- Experience with generative AI or retrieval-augmented generation (RAG) frameworks in pharma/biotech setting
- Experience in Agile software development methodologies
- Experience in End-to-End testing as part of Test-Driven Development
- Willingness to work on Full stack Applications
- Experience working with biomedical or scientific data (e.g., PubMed, clinical trial registries, internal regulatory databases)
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Description
Basic Qualifications:
Preferred Qualifications:
Good to Have Skills
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