• 4–6 years of overall software engineering or ML experience. • 2+ years of hands-on experience in Generative AI, LLMs, or applied NLP. • Strong programming skills in Python (required); experience with JavaScript/TypeScript is a plus. • Practical experience with: • LLM APIs and frameworks (e.g., OpenAI-compatible APIs, LangChain, LlamaIndex) • Prompt engineering and RAG architectures • Vector databases (e.g., Pinecone, FAISS, Weaviate, Chroma) • Experience deploying ML/AI solutions in cloud environments (AWS, Azure, or GCP). • Solid understanding of data structures, APIs, and system design. • Ability to write clean, maintainable, and testable code. • Design, develop, and deploy Generative AI applications using LLMs (e.g., GPT-based, open-source models). • Build and optimize prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning pipelines. • Integrate GenAI models into production systems using APIs, microservices, and cloud platforms. • Collaborate with product managers, data scientists, and backend engineers to translate business needs into AI-driven solutions. • Evaluate model performance, mitigate hallucinations, and improve accuracy, safety, and latency. • Implement monitoring, logging, and feedback loops for continuous model improvement. • Stay current with emerging GenAI frameworks, tools, and best practices. • Ensure compliance with security, privacy, and responsible AI guidelines. • Experience in RAG based applications using LangGraph and FastAPI. • Familiarity with MLOps practices (CI/CD for ML, model versioning, monitoring). • Knowledge of NLP concepts such as embeddings, transformers, and tokenization.