Role Overview :

We are looking for an AI Engineer to develop intelligent search and machine learning solutions for large-scale business applications. The role involves NLP, semantic search, LLMs, RAG, recommendation systems, and production ML.

Key Responsibilities :

- Build and improve enterprise search, ranking, personalization, and recommendation systems.

- Develop ML models for relevance scoring, semantic retrieval, classification, and NLP use cases.

- Design embeddings, indexing, taxonomy, ontology, and metadata solutions to improve search quality.

- Build RAG and vector-search solutions using LLMs and enterprise knowledge sources.

- Work with business teams across Loans, Insurance, and Investments to deliver AI-powered features.

- Analyze user behavior, model metrics, and search performance to improve relevance and accuracy.

- Develop and deploy production-ready ML solutions using Python and SQL.

- Collaborate with engineering, product, and design teams on AI initiatives and PoCs.

- Follow Git, CI/CD, testing, monitoring, and production ML best practices.

Required Skills :

- 3+ years of hands-on experience in AI/ML, Data Science, NLP, Deep Learning, or Applied AI.

- Strong Python, SQL, data analysis, feature engineering, and ML development experience.

- Hands-on experience with PyTorch, TensorFlow, Keras, Scikit-learn, or similar frameworks.

- Experience with NLP, embeddings, semantic search, recommendations, or document intelligence.

- Practical experience with LLMs such as GPT, Llama, Mistral, Claude, Gemini, or equivalent.

- Hands-on experience with RAG, vector search, embeddings, chunking, indexing, and knowledge retrieval.

- Experience with Git, CI/CD, production ML systems, and scalable deployments.

- B.E./B.Tech or M.Tech from IITs, NITs, or BITS.

- Candidate age should be below 28 years.

Good to Have :

- Experience with MLflow, Kubeflow, Airflow, Prefect, Feature Stores, or MLOps/LLMOps.

- Exposure to Vector Databases, Spark/PySpark, Docker, Kubernetes, or cloud platforms such as Azure, AWS, or GCP.

- Experience in FinTech, Banking, Lending, Risk/Fraud Analytics, SaaS, or AI-first product companies.

Compensation :

- 75% fixed + 25% variable, as per company policy.