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AI Innovation Leader

3 weeks ago

Cochin, Kerala, India beBeeArtificialIntelligence Full time US$ 1,50,000 - US$ 2,00,000
Senior AI/ML Engineer

We are seeking a highly creative and hands-on Senior AI/ML Engineer to join our dynamic team.

This role requires building from the ground up—someone passionate about designing agent-driven architectures and inventing new frameworks to solve real-world problems.

  • Key Responsibilities:
  • Design, develop, optimize, and deploy robust ML models and pipelines into production using strong Python and SQL expertise.
  • Architect and implement innovative, scalable, agent-driven AI solutions, emphasizing creative problem-solving and thinking beyond standard libraries.
  • Leverage a strong foundation in linear algebra, calculus, probability, and statistics to solve challenging ML problems.
  • Implement a variety of ML algorithms, including Linear/Logistic Regression, Tree-based models (XGBoost), Clustering, and Anomaly Detection.
  • Apply geometric computation and 3D spatial reasoning to unique problem domains—a key advantage in this role.
  • Champion software excellence through Test-Driven Development (TDD) and contribute to our cloud-native AWS infrastructure.
  • Work cross-functionally as a senior technical member, driving projects from conception to deployment.
Requirements:
  • 5+ years of proven experience as a hands-on Senior AI/ML Engineer or Senior Developer.
  • Expert-level proficiency in Python and SQL.
  • A demonstrated portfolio of creative problem-solving and the ability to independently devise new approaches.
  • Hands-on experience in designing or implementing agent-driven architectures or frameworks.
  • Solid familiarity with Test-Driven Development (TDD) practices.
  • Experience with deploying models on cloud platforms; AWS is highly preferred.
  • Strong background in applying geometric concepts, simulations, or spatial reasoning through code.
Nice to Have:
  • Experience with reinforcement learning (RL) or autonomous agents.
  • Familiarity with modern MLOps tools and practices (e.g., Docker, Kubernetes, MLflow, SageMaker).
  • A background in a research-heavy or innovation-focused environment.