Artificial Intelligence Engineer

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.