LLM Full Stack Engineer

THE WORK: Explore the fascinating world of Large Language Models and immerse yourself in a role where your independent contributions will shape innovative solutions. You will actively participate in discussions and collaborate to provide thoughtful answers to complex challenges. This opportunity invites you to grow as a subject matter expert while engaging with cutting-edge technology. Join us in this exciting journey and make a meaningful impact.

Responsibilities:

Design and develop AI/ML and Generative AI solutions using Python

Build and deploy applications leveraging Large Language Models (LLMs)

Develop end-to-end pipelines including data ingestion, preprocessing, model integration, and deployment

Implement prompt engineering and fine-tuning strategies for LLMs

Integrate AI services into web/mobile/backend applications via APIs

Work with vector databases and embeddings for semantic search and RAG architectures

Optimize models and pipelines for performance, latency, and cost

Collaborate with cross-functional teams to translate business requirements into AI solutions

Ensure responsible AI practices including data privacy, bias mitigation, and explainability

Monitor and improve model performance in production environments

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Required Skills & Qualifications

6-8 years of experience in software engineering / AI / ML, Python roles,

Strong programming expertise in Python

Hands-on experience with ML/DL frameworks like PyTorch or TensorFlow

Experience with LLM frameworks such as LangChain or LlamaIndex

Solid understanding of NLP concepts and transformer-based models

Experience with REST APIs and microservices architecture

Familiarity with vector databases (e.g., Pinecone, FAISS, Weaviate)

Experience with version control systems like Git

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Preferred Qualifications

Experience working with APIs from providers like OpenAI or open-source LLMs (Llama, Mistral)

Knowledge of Retrieval-Augmented Generation (RAG) architectures

Experience with model fine-tuning and evaluation techniques

Exposure to cloud platforms (AWS / GCP) for deployment (excluding Azure)

Understanding of containerization tools like Docker

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Soft Skills

Strong analytical and problem-solving skills

Ability to translate complex AI concepts into practical solutions

Good communication and stakeholder management skills

Ability to work independently and in collaborative environments

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Nice to Have

Experience with AI agents / autonomous workflows

Knowledge of MLOps practices and CI/CD for ML pipelines

Exposure to multimodal AI (text, image, audio models)

Experience with real-time AI applications (chatbots, voice bots, copilots)