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.