CA0236 - AI/ML Developer & Data Scientist

Job Description

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.