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Overview
Our client, an industry leader in AI-driven software solutions, is seeking an experienced AI / Machine Learning Engineer to design, build, and deploy intelligent systems that power predictive analytics, natural language understanding, and generative AI applications.
This role combines deep technical expertise in ML modeling and production deployment with a practical understanding of how to translate data into business impact. The ideal candidate will work cross-functionally with data engineers, software developers, and product teams to integrate models into scalable, high-performance applications.
Responsibilities
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Design, develop, and deploy machine learning models for classification, prediction, recommendation, and generative use cases.
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Build and maintain data pipelines for training and inference using tools such as TensorFlow, PyTorch, or scikit-learn.
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Fine-tune and optimize large language models (LLMs) or foundation models for domain-specific applications.
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Collaborate with product and software engineering teams to integrate models into production environments (APIs, microservices, etc.).
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Implement MLOps practices — version control, automated retraining, and continuous deployment for ML workflows.
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Monitor model performance, bias, and drift; propose retraining or architecture improvements.
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Stay up to date with emerging AI research and apply new methods to real-world business challenges.
Qualifications
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Bachelor’s or Master’s degree in Computer Science, Data Science, Applied Mathematics, or related field (PhD a plus).
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3–8 years of experience in applied machine learning, AI engineering, or data science roles.
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Strong proficiency in Python and ML libraries such as TensorFlow, PyTorch, or Hugging Face.
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Experience with data processing frameworks (Pandas, NumPy, Spark) and modern ML tooling (MLflow, Kubeflow, or SageMaker).
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Solid understanding of statistics, feature engineering, model evaluation, and optimization techniques.
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Familiarity with cloud platforms (AWS, GCP, Azure) and containerized deployment (Docker, Kubernetes).
Preferred
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Experience with Generative AI, LLMs, or multi-modal AI (text, vision, audio).
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Familiarity with vector databases (Pinecone, FAISS, Milvus) or retrieval-augmented generation (RAG) pipelines.
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Background in MLOps or data engineering supporting production-grade AI systems.
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Ability to communicate complex technical concepts clearly to non-technical audiences.
Compensation
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Estimated Base Salary (Bay Area): $190,000 – $230,000 + bonus/equity potential
(depending on experience, specialization, and deployment expertise)
Why This Role
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Join a fast-growing company at the center of the Bay Area’s AI innovation ecosystem.
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Work on impactful, high-visibility projects shaping the next generation of generative AI and applied ML systems.
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Collaborate with top engineers, data scientists, and thought leaders in the field.
