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Role Overview We are looking for an experienced GenAI / AI-ML Engineer with strong hands-on expertise in Python, machine learning, deep learning, Large Language Models, Retrieval-Augmented Generation, and agentic AI systems. The selected candidate will be responsible for designing, developing, and deploying scalable AI-powered applications. The role requires practical experience in building production-ready RAG pipelines, LLM-powered applications, REST APIs, machine-learning models, and cloud-based AI solutions using AWS. Key Responsibilities ● Design, develop, test, and deploy scalable AI, machine-learning, deep-learning, and Generative AI solutions. ● Build and optimise Retrieval-Augmented Generation pipelines using modern frameworks, embedding models, and vector databases. ● Develop LLM-powered applications using prompt engineering, AI agents, LangGraph, and multi-agent workflows. ● Fine-tune, evaluate, deploy, and monitor machine-learning and deep-learning models. ● Build REST APIs and backend services for AI applications using FastAPI or similar frameworks. ● Design data-preprocessing, feature-engineering, model-training, and model-evaluation pipelines. ● Integrate structured and unstructured data sources to deliver accurate and context-aware AI solutions. ● Implement semantic search and document-retrieval architectures. ● Evaluate RAG and Generative AI solutions using appropriate quality and performance metrics. ● Collaborate with Data Engineering, DevOps, Product, and other cross-functional teams. ● Ensure the scalability, reliability, security, and performance of AI applications in production environments. ● Follow software-engineering best practices, coding standards, version-control processes, and Agile methodologies. ● Troubleshoot model, API, data-pipeline, and production-performance issues. Requirements Mandatory Skills Programming and Backend Development ● Strong hands-on experience in Python. ● Strong working knowledge of SQL. ● Experience developing REST APIs using FastAPI or similar Python frameworks. ● Good understanding of object-oriented programming, modular development, testing, and software-engineering best practices. ● Experience working in Agile development environments. Machine Learning and Deep Learning Hands-on experience with: ● Scikit-learn ● TensorFlow ● PyTorch ● Keras Strong understanding of: ● Regression ● Classification ● Clustering ● Feature engineering ● Data preprocessing ● Model evaluation ● Hyperparameter tuning ● Model deployment and monitoring NLP and Generative AI ● Minimum one year of hands-on experience working on GenAI or LLM-based projects. ● Strong understanding of Large Language Models and Natural Language Processing concepts. ● Experience in prompt engineering and prompt optimisation. ● Hands-on experience designing and implementing RAG architectures. ● Experience building agentic AI or multi-agent applications. ● Practical experience with: ○ LangChain ○ LangGraph ○ OpenAI APIs ○ Hugging Face ○ LangSmith RAG and Vector Databases ● Experience working with vector databases and similarity-search technologies, including: ○ Pinecone ○ FAISS ● Knowledge of embedding models, chunking strategies, semantic search, document retrieval, and reranking. ● Experience evaluating RAG solutions using metrics or frameworks such as: ○ RAGAS ○ BLEU ○ ROUGE AWS and DevOps Hands-on experience with AWS services such as: ● Amazon EC2● Amazon S3 ● Amazon SageMaker ● Amazon Bedrock Experience working with: ● Docker ● Git ● JIRA ● CI/CD pipelines Preferred Qualifications ● Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline. ● Experience deploying AI and LLM applications in production environments. ● Understanding of LLM observability, hallucination control, guardrails, latency o