AI/ML Developer & Data Scientist

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.

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• 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.