Associate Lead - AI/ML Engineer ( Python AL/ML)

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

Strong AI Engineer / Machine Learning Engineer profiles.

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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.

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Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

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Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

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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.

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Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

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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.

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Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

Roles & Responsibilities

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.