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Global brand, metro location, mid-level GenAI role with broad requirements.
Role demands specialized ML/GenAI expertise, limiting transferability across non-ML domains.
Explicit 6–8 years plus specific GenAI, LLM, and MLOps technology requirements.
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Design, develop, and deploy machine learning and generative AI (GenAI) solutions including Retrieval-Augmented Generation (RAG) pipelines and Large Language Models (LLMs) in production.
Collaborate cross-functionally with Engineering, Product, and SMEs to implement AI-driven features, monitor model performance, and conduct experimentation including A/B testing.
Contribute to AI system architecture design and implement scalable ML pipelines with Python, handling structured and unstructured data sources.
6–8 years of experience in data science or related fields with focus on AI/ML and GenAI technologies.
Advanced proficiency in Python and experience with machine learning techniques (supervised, unsupervised, NLP).
Hands-on experience with GenAI including prompt engineering, LLM fine-tuning, RAG architecture, vector search, and related ML & AI frameworks (LangChain, LlamaIndex, PyTorch, TensorFlow, HuggingFace, etc).
Experience with cloud platforms (AWS, Azure, GCP), SQL, data querying, CI/CD for ML deployments, and model tracking tools like MLflow.
Strong expertise in advanced ML and GenAI techniques, including fine-tuning large language models and building RAG pipelines for real-world applications.
Experience working in complex, production-scale AI/ML environments with cross-functional collaboration and delivering AI-driven business solutions.
Familiarity with AI model monitoring, MLOps practices, and scalable pipeline development using modern tools and cloud infrastructure.