Gen AI Developer

Role

Required Technical Skill Set (MustHave)

Desired Experience Range

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.
• Bachelors 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