AI Engineer (FastAPI, LLMs, MongoDB) 3 years Experience

Overview

We’re looking for a hands-on AI Engineer passionate about building scalable, production-ready AI systems — from model integration and fine-tuning to deploying intelligent micro-agents across enterprise workflows.
You’ll collaborate with our research, backend, and product teams to push the limits of what AI can do in real-world business settings.

Key Responsibilities

Develop, fine-tune, and integrate LLMs, vision models, and multimodal pipelines into production systems.

Build agentic workflows that automate enterprise operations, document understanding, and conversational decision systems.

Implement data pipelines, vector databases, embeddings, and retrieval systems.

Collaborate with front-end and DevOps teams to ensure efficient deployment using FastAPI, Docker, Kafka, and AWS.

Contribute to R&D on custom model training, inference optimization, and real-time orchestration.

Maintain clean, modular, and well-documented code for long-term scalability.

Requirements

Experience (1–3 years) in applied AI/ML, with a focus on building production-ready systems.

Strong in Python, FastAPI, PyTorch/TensorFlow, and OpenAI / HuggingFace ecosystem.

Familiarity with LangChain, LlamaIndex, RAG pipelines, or agentic frameworks.

Hands-on with cloud deployment (AWS / Azure), Docker, and MongoDB.

Understanding of computer vision / image models (bonus).

Ability to thrive in a fast-paced, research-driven, startup environment.

Nice-to-Have

Contributions to open-source AI tools or personal ML projects.

Experience working on document intelligence, video generation, or voice-AI systems.

Background in data engineering or system optimization.