AI Engineer

Responsibilities 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 Strong Data Scientist / AI Engineer / Machine Learning Engineer profiles. Mandatory (Experience 1) – Must have minimum 5+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions. Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications. Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent. Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases. Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models. Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions. Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems. Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy. Mandatory (Age) - Candidate's Age should be below 30 Years Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks. Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems.. Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture. Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies Skills: analytics,learning,nlp,models,machine learning,ai,ci,optimization,data,cd,data scientist,ml