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Mid-level ML role with metro location and Sequoia-backed brand increases applicant competition moderately.
Role requires deep ML/LLM and MLOps expertise, so skills are less transferable across non-ML roles.
Explicit 5–8 years plus many mandatory ML, LLM, MLOps, and cloud requirements.
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Design, develop, and deploy scalable AI and machine learning solutions including large language model (LLM) applications using frameworks like LangChain and vector databases for RAG pipelines.
Lead transition of models from research to production and manage comprehensive MLOps pipelines (training, testing, deployment, monitoring, retraining).
Collaborate with business stakeholders to translate requirements into AI-driven outcomes and mentor junior AI engineers promoting best AI engineering practices.
5–8 years of experience in AI/ML engineering, including at least 2 years leading AI initiatives.
Strong expertise in Python, TensorFlow, PyTorch, Hugging Face Transformers; hands-on experience with LangChain or similar LLM application frameworks.
Proficiency in vector databases, RAG architectures, prompt engineering, and MLOps tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes).
Minimum Master’s Degree in Engineering, Computer Science, Mathematics, Computational Statistics, Operations Research, Machine Learning or related technical field.
Experienced in end-to-end AI/ML solution architecture and production deployment with demonstrated leadership in AI projects.
Skilled in advanced LLM and RAG technologies, MLOps pipeline development, and integrating AI into enterprise applications via APIs and microservices.
Able to balance technical innovation with responsible AI principles including bias mitigation, fairness, and explainability, while mentoring teams and engaging with business stakeholders.