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Specialized LLM/production skills but metro location and attractive role create moderate competition.
ML/AI technical skills transfer across industries, but LLM/agentic production experience biases toward tech/SaaS employers.
Explicit 7–13 years requirement plus mandatory LLM, deployment, and observability skills.
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Lead design and development of advanced machine learning models and scalable AI/ML pipelines encompassing data ingestion, feature engineering, model training, deployment, and monitoring.
Develop and operationalize AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI systems in collaboration with engineering teams.
Analyze complex data sets to generate actionable insights influencing product strategy and drive adoption of ML best practices and lifecycle management across the organization.
7 to 13 years of experience in Data Science, Machine Learning, or related roles.
Proficient in Python and SQL with hands-on experience in ML frameworks like Scikit-learn, Pandas, NumPy, Hugging Face, and transformer-based models.
Experience building and deploying GenAI applications with LLMs, RAG, vector databases, and agent orchestration frameworks (e.g., LangChain).
Hands-on experience deploying and monitoring AI/ML models on AWS; knowledge of Azure or GCP is a plus.
Strong technical depth in both supervised/unsupervised learning, NLP, recommendation systems, and time-series analysis with cross-domain expertise.
Demonstrated ability to translate business problems into AI/ML solutions with experience measuring business impact in SaaS or data-driven environments.
Experience in building AI-driven products in Agile environments with familiarity in SaaS business metrics, go-to-market analytics, and scalable data infrastructure.