About this role
Lead Machine Learning Engineer
Sequoia
Job descriptionApplication
About the role
Key Responsibilities
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Machine Learning & Data Science
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Design, develop, and deploy end-to-end ML solutions at scale.
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Build and optimize predictive models, recommendation systems, NLP solutions, and
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deep learning applications.
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Drive the complete data science lifecycle:
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Problem formulation
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Data exploration
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Feature engineering
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Model training
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Evaluation
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Generative AI
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Build enterprise-grade GenAI applications using:
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OpenAI
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Azure OpenAI
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Anthropic Claude
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Llama
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Mistral
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Gemini
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Design and implement:
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RAG architectures
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Agentic AI systems
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Multi-agent frameworks
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Prompt engineering strategies
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Fine-tuning pipelines
Key Responsibilities
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Design, build, deploy, and monitor production-grade ML solutions
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Develop AI/ML applications using modern ML and GenAI frameworks
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Build and optimize end-to-end ML pipelines
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Collaborate with Product and Engineering teams to deliver business impact
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Drive best practices in MLOps, model governance, and scalability
Preferred Skills:
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Python, SQL, Spark
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ML/DL frameworks (PyTorch, TensorFlow, Scikit-learn)
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LLMs, RAG, Agentic AI
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Docker, Kubernetes, Cloud Platforms (AWS/Azure/GCP)
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MLOps and model deployment
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Production deployment
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Excellent communication skills and ability to work with diverse stakeholders
What Sets You Apart:
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Experience optimizing LLMs for production (cost, latency, scaling)
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Track record of maintaining AI systems in production
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Ability to balance innovation with practical business needs
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Experience with HR/people analytics domain
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Must-have skills
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Machine LearningGENAIProduction
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Good-to-have skills
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Gen AI