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.
