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Senior AI/ML Innovator

2 weeks ago


Bhavnagar, Gujarat, India beBeeAI Full time US$ 1,50,000 - US$ 1,75,000
Job Opportunity

A leading US-based AI Data Solutions provider is at the forefront of AI and ML innovation, developing cutting-edge, cloud-native data solutions to tackle complex challenges and drive intelligent technology.

The Role

We seek a Senior AI/ML Engineer with strong Python and SQL expertise to join our dynamic team. As a problem-solver who thrives on building from scratch, you'll design agent-driven architectures, invent new frameworks, and turn complex mathematical concepts into production-ready, scalable solutions.

Key Responsibilities:
  • End-to-End Ownership: Design, develop, optimize, and deploy robust ML models and pipelines into production.
  • Innovate & Build: Architect and implement innovative, scalable AI solutions using creative problem-solving skills.
  • Apply Foundational Math: Leverage linear algebra, calculus, probability, and statistics to solve challenging ML problems.
  • Algorithm Mastery: Implement various ML algorithms, including Linear/Logistic Regression, Tree-based models (XGBoost), Clustering, and Anomaly Detection.
  • Geometric & Spatial Reasoning: Apply geometric computation and 3D spatial reasoning to unique problem domains.
  • Drive Best Practices: Champion software excellence through Test-Driven Development (TDD) and contribute to our cloud-native AWS infrastructure.
Requirements
  • 5+ years of experience as a hands-on Senior AI/ML Engineer or Senior Developer.
  • Expert-level proficiency in Python and SQL.
  • Demonstrated portfolio of creative problem-solving and ability to 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.