Senior AI Engineer - RAG/LLM

Job Summary :

We are looking for a Senior GenAI / AI Engineer with strong hands-on experience in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Machine Learning and Deep Learning. The role involves designing, developing, integrating, deploying and scaling production-ready AI/ML solutions, including RAG applications, AI chatbots and AI agents.

The candidate will work closely with data, infrastructure and business/client stakeholders to translate requirements into scalable AI solutions and take them from development through production, evaluation and monitoring.

Key Responsibilities :

- Build and scale Generative AI solutions using LLMs, RAG and AI Agents.

- Develop AI/ML solutions using Machine Learning and Deep Learning.

- Build and deploy RAG-based applications and AI chatbots.

- Train, fine-tune and optimize AI/ML models.

- Productionize ML/AI pipelines and Python services.

- Integrate AI solutions with business applications and APIs.

- Work with data teams on datasets, SQL and ETL.

- Implement AI/ML solutions across AWS, GCP and/or Azure.

- Apply MLOps practices including CI/CD, Docker, Kubernetes and MLflow.

- Monitor and evaluate model and RAG performance and improve accuracy.

- Apply Responsible AI principles, including appropriate handling of PII and model risks.

- Understand business and technical requirements and convert them into solution designs.

- Partner with data, infrastructure and business/client stakeholders to deliver scalable AI solutions.

Required Skills & Experience :

- 8 - 12 years of overall IT experience.

- 5+ years of hands-on AI/ML development experience.

- Advanced Python programming.

- Strong experience with LLMs and Generative AI.

- Hands-on experience with RAG and embedding models.

- Understanding and practical experience with AI Agents / Agentic AI.

- Strong knowledge of Machine Learning and Deep Learning.

- Experience with model training, fine-tuning and optimization.

- Strong understanding of model evaluation and performance monitoring.

- Experience with SQL, ETL and data engineering.

- Experience integrating AI solutions through APIs and business applications.

- Experience with AWS, GCP or Azure.

- Experience with MLOps, CI/CD, Docker, Kubernetes and/or MLflow.

- Strong statistical, analytical and problem-solving skills.

- Understanding of Responsible AI, PII and AI model risks.

- Strong requirement elicitation, analysis and solution-design capabilities.

- Good communication and stakeholder-management skills.