- Contribute to the development and optimization of enterprise-wide search systems and models.

- Design and implement algorithms to improve indexing, query relevance, and search accuracy.

- Support taxonomy, ontology, and metadata model creation for better search outcomes.

- Collaborate with business units (Loans, Insurance, Investments) to build AI-enabled search features.

- Conduct analysis of user behavior and system metrics to refine search performance.

- Work with engineers, product managers, 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 software engineering including CI/CD, testing, and monitoring.

- Keep abreast of emerging developments in AI/ML to apply them in practical solutions.

Ideal Candidate :

Mandatory Experience :

- 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

- Strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

- Experience with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

- Hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

- Experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

- Hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

- Experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

Preferred Experience :

- Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.

- Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems.

- Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.

Eligibility :

- Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies.

- B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are considered.