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Sr. AI / ML Engineer

Sr. AI / ML Engineer

SoulaxHyderabad, India
9 days ago
Job description

Sr AI / ML Engineer :

We’re looking for a Senior AI / ML Engineer with a strong foundation in geometric reasoning, hands-on Python and SQL expertise, and a flair for creative problem-solving. This role is ideal for someone who thrives on building innovative, agent-driven solutions and can independently devise new frameworks or approaches to solve complex problems.

Roles & Responsibilities :

  • Develop, optimize, and deploy end-to-end ML models with strong Python and SQL expertise .
  • Apply linear algebra, calculus, probability, and statistics to solve real-world ML problems.
  • Holds a strong understanding of Computer Vision methodologies and its practical applications.
  • Design and implement ML algorithms such as Linear / Logistic Regression, Trees, XGBoost, Clustering, and Anomaly Detection.
  • Build scalable, agent-driven AI solutions with a focus on innovation and independent problem-solving.
  • Strong experience in geometric concept application and hands-on coding over theoretical research.
  • Practice Test-Driven Development with exposure to AWS cloud deployment.
  • Collaborate cross-functionally as a senior developer, driving creative and practical AI / ML engineering solutions.

Required Skills :

Proven experience as a hands-on Senior Developer or AI / ML Engineer (Ph.D. not mandatory, but strong experience and creativity are key).

Strong background in Python programming and SQL.

Demonstrated ability to creatively solve problems and think beyond standard solutions.

Experience designing or implementing agent-driven architectures or frameworks.

Ability to work independently and drive innovation.

Familiarity with test-driven development (TDD) practices.

Experience with AWS cloud platforms is a plus.

Exposure to geometric computation, simulations, or 3D spatial reasoning is a strong advantage.

Nice to Have :

  • Experience with reinforcement learning or autonomous agents.
  • Familiarity with modern ML Ops practices.
  • Previous work in a research-heavy or innovation-focused environment.
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