Overview

You will advance enterprise search infrastructure through algorithm design and model optimization. Improve indexing, query relevance, and search accuracy across systems. Build metadata models, taxonomies, and ontologies to enhance search capabilities.

Responsibilities

• You will advance enterprise search infrastructure through algorithm design and model optimization. Improve indexing, query relevance, and search accuracy across systems.
• Build metadata models, taxonomies, and ontologies to enhance search capabilities.

Qualifications

• Demonstrated expertise in Machine Learning and AI engineering domains.
• Mandatory (Experience 1) – Minimum 3+ years of direct hands-on work in Machine Learning, Data Science, Applied AI, Deep Learning, NLP, or Generative AI solution development.
• Mandatory (Experience 2) – Proven hands-on proficiency with Python, SQL, data analysis, feature engineering, model development, and production-level ML systems.
• Mandatory (Experience 3) – Working experience with Deep Learning and ML frameworks including PyTorch, TensorFlow, Keras, Scikit-learn, or comparable tools.
• Mandatory (Experience 4) – Hands-on background in semantic search, NLP, embeddings, text classification, document understanding, recommendation engines, or comparable AI/ML applications.
• Mandatory (Experience 5) – Experience deploying Large Language Models including GPT, Llama, Mistral, Claude, Gemini, Phi, or analogous foundation models.
• Mandatory (Experience 6) – Hands-on implementation experience with RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.
• Mandatory (Experience 7) – Experience with Git, CI/CD practices, production systems, and large-scale AI/ML infrastructure.
• Mandatory (CTC) – CTC structure comprises 75% fixed and 25% variable compensation per organizational guidelines.
• Mandatory (Age) - Applicant age must be below 28 Years.
• Preferred (Experience 1) – Familiarity with MLOps or LLMOps tools such as MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, or Model Registry systems.
• Preferred (Experience 2) – Background in Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems.
• Preferred (Experience 3) – Knowledge of Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.
• Preferred (Company) – Background from AI-focused startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, SaaS, Product Companies, or data-driven technology organizations.
• Mandatory ( Pedigree) - B.TECH or M.TECH from Tier 1 Colleges (IIT, NIT, BITS) preferred.

Benefits

• Your professional drive matters to us, and we recognize exceptional talent and ambitious contributions. Bajaj Finance Limited ranks among India's most diversified Non-banking financial companies and Asia's leading 10 Large workplaces. We offer pathways to advancement for motivated professionals across our 500+ operational centers throughout India.