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CA0236 - AI/ML Developer & Data Scientist

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Role Overview

We are looking for a dynamic professional with a strong blend of AI/ML development (70%) and Data Science expertise (30%) or vice versa. The ideal candidate will design and deploy intelligent solutions, build predictive models, and manage large-scale data workflows. This role suits someone who thrives in innovation-driven environments and wants to contribute to cutting-edge AI initiatives.

Key Responsibilities

  • AI/ML Model Development:
  • Design, develop, and deploy machine learning models and AI solutions for real-world applications.
  • Data Engineering & Integration:
  • Build and optimize data pipelines to support AI/ML workflows and ensure seamless integration with existing systems.
  • Generative & Agentic AI Exposure (Preferred):
  • Work on emerging AI technologies such as Generative AI and Agentic AI for advanced use cases.
  • Data Analysis & Visualization:
  • Collect, clean, and preprocess large datasets; perform statistical analysis and create dashboards to derive actionable insights.
  • Collaboration:
  • Partner with engineers, data scientists, and product teams to deliver scalable AI-driven solutions.
  • Performance Optimization:
  • Fine-tune models for accuracy, efficiency, and scalability in production environments.

Required Qualifications

  • Experience:
  • Minimum 3 years in AI/ML development with exposure to data engineering concepts.
  • Technical Skills:
  • Programming: Proficiency in Python or R; Deep knowledge of SQL and data manipulation.
  • AI/ML Frameworks: Hands-on experience with TensorFlow, PyTorch, or similar.
  • Data Handling: Strong skills in managing large datasets and building ETL pipelines.
  • Cloud Platforms: Familiarity with AWS, GCP, or Azure for AI/ML deployments.
  • APIs: Experience in developing REST APIs for model integration.
  • Core Competencies:
  • Analytical mindset, problem-solving skills, and ability to work in agile environments.

Preferred Skills

  • Exposure to Generative AI, Agentic AI, NLP, or Deep Learning.
  • Experience with Big Data technologies (Hadoop, Spark).
  • Understanding of MLOps and deployment of models in production.

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