Responsibilities :
- Develop,Design, develop, and deploy scalable Machine Learning models for predictive analytics, classification, regression, recommendation, clustering, and forecasting problems.
- Build end-to-end AI pipelines covering data preprocessing, feature engineering, model training, evaluation, deployment, and monitoring.
- Develop and fine-tune Large Language Models (LLMs) for enterprise use cases.
- Design and implement Retrieval-Augmented Generation (RAG) architectures using vector databases and embedding models.
- Build autonomous AI agents capable of planning, reasoning, memory management, and tool usage.
- Contribute to the establishment of best practices for efficient utilization of generative models in business and automation contexts.
- Work with leadership and stakeholders to identify AI opportunities and promote strategy.
- Be able to deliver solutions following modern software development practices.
Required Skills:
- Bachelor's degree in computer science, IT, AI or relevant discipline
- Must have minimum of 1 year of work experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps/LLMOps, Optimization techniques, and AI solutions.
- Specific experience in GenAI and Conversational AI – LLMs, frameworks like Lang chain, Llama Index etc, Vector DBs like Pinecone, Elasticsearch, etc.
- Solid proficiency in Python programming and related AI/ML libraries.
- Experience with cloud platforms (AWS, Azure, or GCP) for deploying and scaling AI/ML solutions.
- Experience with AI model evaluation, prompt optimization, and implementation of Responsible AI practice
- Knowledge of API integration, microservices architecture, and production-grade AI application deployment.
- Design GenAI application, integrating advanced models like OpenAI’s GPT, BERT or Open Source LLMs.
- Deep understanding of the RAG architecture and its implementation.
- Knowledge in SQL generation using LLM is preferred.
- Ability to write high-quality, maintainable code and work collaboratively.
- Knowledge of Agile software development lifecycle, including experience of applying change control procedures and disciplines required.
- Problem solving with strong analytical skills with the ability to analyse complex requirements and define technical / functional advanced solutions.
- Excellent communication skills in both verbal and written English
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