Job Title

Role : Gen AI Developer Required Technical Skill Set (MustHave) : Gen AI / LLM /Agentic AI Desired Experience Range 4 to 8 Yrs Location of Requirement : PAN India Must-Have · Strong programming skills in Python with experience in building ML/NLP applications. · Hands-on experience with Deep Learning, Machine Learning, and Natural Language Processing. · Proficiency in working with LLMs (e.g., GPT,Gemini, LLaMA, Mistral) and RAG architecture. · Experience with Agentic AI frameworks and autonomous agent design. · Familiarity with FastAPI for building RESTful APIs. · Experience with CI/CD pipelines and deploying solutions on Azure Cloud, GCP and AWS · Solid understanding of data structures, algorithms, and software engineering principles. · Bachelor’s or master’s degree in computer science, AI, Data Science, or related field. · Retrieval Augmented Generation (RAG): Hands-on experience in building and optimizing RAG pipelines from scratch · Expert-level proficiency and hands-on experience with LangChain and/or LlamaIndex for building complex LLM applications, including chains, agents, memory, and tool integration. Good-to-Have · Excellent Communication Skills · Strong Software Engineering Background (Productionizing the models) · Hands-on experience with data science tools · Problem-solving aptitude · Analytical mind and great business sense. Responsibility of / Expectations from the Role · Design and implement GenAI solutions leveraging LLMs, RAG pipelines, and Agentic AI architectures. · Develop and fine-tune deep learning models for NLP tasks using frameworks like PyTorch or TensorFlow. · Build scalable APIs using FastAPI to serve AI models and integrate with enterprise systems. · Collaborate with data scientists and ML engineers to deploy models using CI/CD pipelines on Azure. · Optimize model performance and ensure robustness in production environments. · Stay updated with the latest research and advancements in generative AI and deep learning. · Scalability & Performance: Ability to write efficient code and consider scalability and latency implications for Gen AI applications. · Containerization: Practical experience with Docker for packaging and deploying applications.