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Known global brand, mid-level ML/GenAI role in a metro with broad demand increases competition.
Requires specialized ML/GenAI expertise, though technical skills remain transferable across industries.
Explicit 6–8 years plus comprehensive GenAI, ML, and infrastructure toolset creates strict screening.
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Design, develop, and deploy machine learning and Generative AI solutions including Retrieval-Augmented Generation (RAG) pipelines and fine-tuned Large Language Models (LLMs).
Implement scalable ML pipelines in production using Python and work with structured and unstructured data sources.
Collaborate with Engineering, Product, and SMEs to deliver AI-driven features and monitor model performance and reliability in production environments.
6–8 years of relevant work experience in data science and AI.
Advanced proficiency in Python and experience with machine learning (supervised/unsupervised learning, NLP).
Experience with Generative AI technologies including LLMs, prompt engineering, embeddings, and RAG architecture.
Familiarity with ML & AI tools such as LangChain/LlamaIndex, Scikit-learn/XGBoost/LightGBM, PyTorch/TensorFlow, HuggingFace/OpenAI APIs, vector databases, SQL, and cloud platforms (AWS/Azure/GCP).
Experienced in handling complex AI projects involving GenAI, RAG, and agentic solutions with a strong operational focus on deploying scalable ML systems.
Demonstrates ability to collaborate cross-functionally with engineering, product teams, and subject matter experts to translate AI capabilities into business applications.
Familiar with model monitoring, evaluation, fine-tuning techniques (LoRA, PEFT, QLoRA), and possibly multi-agent frameworks, preferably with legal, regulatory, or publishing domain exposure.