GEN AI Developer

SN Required Information Details

1 Role Gen AI Developer

2 Required Technical Skill Set (MustHave) Gen AI / LLM /Agentic AI

4 Desired Experience Range 4 to 8 Yrs

5 Location of Requirement Any

6 IOU-Detail Digital-Manufacturing

Desired Competencies (Technical/Behavioral Competency)

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

SN Responsibility of / Expectations from the Role

1 • 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.