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