About the role
Job Title: Data Scientist / AIML Engineer / Gen AI Engineer Location: Gurugram, Haryana (On-site, 5 days a week) Experience: 3 – 4 Years Compensation: ₹15,00,000 – ₹25,00,000 LPA Employment Type: Full-Time
About the Role: We are a fast-growing, product-based technology firm seeking a highly skilled AI/ML professional to join our newly established AI division. You will be instrumental in designing, deploying, and maintaining production-grade AI applications, focusing on scalable Gen AI and Machine Learning systems.
Key Responsibilities:
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Design scalable, reliable AI systems with a focus on production readiness, maintainability, and performance.
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Create and maintain Retrieval-Augmented Generation (RAG) pipelines and AI agent frameworks.
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Design reliable LLM workflows using structured outputs, validation, retrieval, and business rules.
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Perform data analysis, model evaluation, root cause analysis, and performance optimization.
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Develop APIs and integrate AI solutions into existing products and platforms.
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Develop automated evaluation, validation, and testing strategies for AI applications.
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Collaborate with cross-functional teams and business stakeholders to define and deliver AI solutions.
Must-Have Qualifications:
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3–5 years of experience building AI/ML applications as an Individual Contributor.
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Full-time Bachelor's or Master’s degree in Computer Science, AI, ML, Data Science, or a related quantitative field (Distance education is not accepted).
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Strong proficiency in Python and robust software engineering fundamentals (clean code, modular design, debugging, and testing).
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Hands-on experience with RAG architectures and NLP concepts.
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Practical experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
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Familiarity with API development and system integration.
Good-to-Have Skills:
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Experience with Vector Databases (Pinecone, Weaviate, FAISS, ChromaDB).
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Knowledge of Cloud platforms (AWS, Azure, GCP) and deployment pipelines (Docker).
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Exposure to Fintech products, platforms, or domain-specific use cases.