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shimoga, India beBeeMachine Full time

AI Architect Role: Empowering Autonomous SystemsWe're on a mission to revolutionize enterprise data paradoxes and pioneer fully autonomous software operations.In this pivotal role, you'll be instrumental in designing our AI-driven systems, directly influencing ML infrastructure, model deployment strategies, and data engineering decisions.You'll have the autonomy to research, experiment, and deploy cutting-edge ML solutions while building significant ownership in our company's future.Priorities:Freedom to Execute: Set your own goals, timelines, and approaches without bureaucratic hurdles.True Ownership: Founding engineers receive substantial equity packages (competitive with senior engineer salaries at scale), ensuring you directly benefit from the value you create.Technical Challenge: Solve complex real-world problems with meaningful impact.Impact-Driven Culture: Directly influence product direction, company culture, and growth strategy.Transparent Leadership: Collaborative environment, weekly strategy sessions, and full visibility into company health and roadmap.Exponential Upside Potential: Your equity stake grows with company success—potential for life-changing returns as we scale.About Us:Expertise Wanted:Passionate builder: Proactive problem-solver who identifies, iterates, and ships solutions.ML veteran: 5+ years building and deploying production ML systems at scale.Deep learning specialist: Hands-on experience with transformer architectures, LLMs, and generative AI.MLOps mastery: Expert in ML pipeline orchestration, model monitoring, and A/B testing frameworks.Programming excellence: Proficiency in Python, with experience in PyTorch/TensorFlow, and familiarity with systems languages (Go, Rust, C++).Technical Requirements:Enterprise AI: Experience deploying ML models in enterprise environments with strict SLAs.Autonomous systems: Background in reinforcement learning, multi-agent systems, or autonomous decision-making.Data engineering: Comfortable with large-scale data processing, feature engineering, and real-time inference.Cloud ML platforms: Experience with AWS SageMaker, Google AI Platform, or Azure ML.Model optimization: Knowledge of model compression, quantization, and edge deployment.

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