Software Engineer E6 - Enterprise Search Whatfix

6.0 - 12.0 years

0 Lacs

bengaluru, karnataka, india

On-site

About the Role We are looking for a passionate and experienced Software Engineer (E5 / E6 level) to join our Enterprise Search team, which is at the core of redefining how users discover and interact with information across Whatfix's digital adoption platform. This is a unique opportunity to solve deep information retrieval and search relevance challenges using scalable infrastructure, cutting-edge NLP, and Generative AI. As an engineer at this level, you'll be expected to operate with strong ownership, lead cross-team technical initiatives, and influence design choices that directly impact user experience and business outcomes. What You'll Do As a senior engineer, you will: Build a 0-to-1 Enterprise Search product with a strong focus on scalability, performance, and relevance. Lead proof-of-concept efforts to validate ideas quickly and align with business goals. Architect and implement robust, maintainable, and scalable systems for indexing, querying, and ranking. Develop data pipelines, implement automation for reliability, and ensure strong observability and monitoring. Work closely with Product Managers and Designers to translate user needs into data-driven, intuitive search experiences. Guide and support junior engineers through code reviews, technical direction, and best practices. Collaborate with cross-functional teams (data, platform, infra) to deliver cohesive and high-impact solutions. What We're Looking For Must-Have Skills: Familiarity with LLMs, RAG pipelines, or knowledge graph integrations. Deep expertise in information retrieval, search engines (Lucene, Elasticsearch, Solr). Experience with vector search, embeddings, and/or neural ranking models (e.g., BERT, Sentence Transformers). Strong programming skills in Java, Python, or Go. Familiarity with scalable data processing frameworks (e.g., Spark, Kafka, Flink). Good understanding of system design, APIs, caching, and performance tuning. Nice-to-Have: Experience with enterprise content connectors (SharePoint, Confluence, Jira, etc.). Experience working in a SaaS, B2B, or product-first environment. Qualifications 6-10+ years of experience building backend systems, infrastructure, or AI platforms at scale. Proven ability to own and deliver complex features independently, collaborate across teams, and mentor peers in a fast-paced environment. Demonstrated experience leading initiatives with significant technical and organizational impact - from setting direction to aligning stakeholders and driving execution.

Posted 2 weeks ago

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Data/Applied Scientist (Search) BAY Area Technology Solutions

3.0 - 5.0 years

8 - 10 Lacs

Hyderabad, Bengaluru, Delhi / NCR

Work from Office

Strong in Python and experience with Jupyter notebooks, Python packages like polars, pandas, numpy, scikit-learn, matplotlib, etc. Must have: Experience with machine learning lifecycle, including data preparation, training, evaluation, and deployment Must have: Hands-on experience with GCP services for ML & data science Must have: Experience with Vector Search and Hybrid Search techniques Must have: Experience with embeddings generation using models like BERT, Sentence Transformers, or custom models Must have: Experience in embedding indexing and retrieval (e.g., Elastic, FAISS, ScaNN, Annoy) Must have: Experience with LLMs and use cases like RAG (Retrieval-Augmented Generation) Must have: Understanding of semantic vs lexical search paradigms Must have: Experience with Learning to Rank (LTR) techniques and libraries (e.g., XGBoost, LightGBM with LTR support) Should be proficient in SQL and BigQuery for analytics and feature generation Should have experience with Dataproc clusters for distributed data processing using Apache Spark or PySpark Should have experience deploying models and services using Vertex AI, Cloud Run, or Cloud Functions Should be comfortable working with BM25 ranking (via Elasticsearch or OpenSearch) and blending with vector-based approaches Good to have: Familiarity with Vertex AI Matching Engine for scalable vector retrieval Good to have: Familiarity with TensorFlow Hub, Hugging Face, or other model repositories Good to have: Experience with prompt engineering, context windowing, and embedding optimization for LLM-based systems Should understand how to build end-to-end ML pipelines for search and ranking applications Must have: Awareness of evaluation metrics for search relevance (e.g., precision@k, recall, nDCG, MRR) Should have exposure to CI/CD pipelines and model versioning practices GCP Tools Experience: ML & AI: Vertex AI, Vertex AI Matching Engine, AutoML, AI Platform Storage: BigQuery, Cloud Storage, Firestore Ingestion: Pub/Sub, Cloud Functions, Cloud Run Search: Vector Databases (e.g., Matching Engine, Qdrant on GKE), Elasticsearch/OpenSearch Compute: Cloud Run, Cloud Functions, Vertex Pipelines, Cloud Dataproc (Spark/PySpark) CI/CD & IaC: GitLab/GitHub Actions Location: Remote- Bengaluru,Hyderabad,Delhi / NCR,Chennai,Pune,Kolkata,Ahmedabad,Mumbai

Posted 3 months ago

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