We are looking for an experienced Data Scientist with expertise in Generative AI, Large Language Models (LLMs), and Agent-based AI systems to design, develop, and deploy advanced AI solutions. The ideal candidate should have strong hands-on experience in Machine Learning, NLP, Deep Learning, and emerging GenAI technologies, with the ability to build scalable AI applications using modern AI platforms and MLOps practices.

Key Responsibilities :

Design, develop, and deploy AI/ML solutions leveraging :

- Generative AI models

- Large Language Models (LLMs)

- Multi-modal AI systems

- AI Agent workflows

Develop and implement machine learning solutions across :

- Natural Language Processing (NLP)

- Deep Learning

- Predictive modeling

- Generative AI applications

Build and optimize LLM-based applications using :

- Prompt engineering techniques

- AI agents and orchestration frameworks

- Retrieval-based AI approaches

Work with Azure AI platforms and services to build enterprise-grade AI solutions.

Apply MLOps best practices for :

- Model deployment

- Monitoring

- Version control

- Performance optimization

Evaluate and improve AI models through experimentation, testing, and performance analysis.

Collaborate with engineering and business teams to translate business requirements into scalable AI solutions.

Required Skills & Experience :

- 6 to 8 years of hands-on experience as a Data Scientist / AI Engineer.

Strong experience in :

- Generative AI concepts and applications

- Large Language Models (LLMs)

- AI Agents / Agentic workflows

- Multi-modal AI systems

Strong understanding and practical experience with :

- Machine Learning

- Natural Language Processing (NLP)

- Deep Learning

- Neural network architectures

- Proficiency in Python programming and AI/ML libraries.

- Good understanding of MLOps practices and productionizing ML models.

- Experience with Azure AI Foundry services and cloud-based AI solutions.

- Strong understanding of GenAI concepts, architectures, models, and industry trends.

Experience working with :

- LLM APIs and AI platforms

- Vector databases and embeddings

- RAG (Retrieval Augmented Generation) architectures

- Prompt engineering techniques

- AI model evaluation frameworks

- Experience building enterprise-level AI solutions from concept to deployment.