Job Opportunities

Software Engineer - AL/ML (3 openings)

Engineer the Full AI Lifecycle

At ThoughtMinds, we believe that an AI Engineer's job doesn't end at the notebook. You will be a Hybrid AI/Systems Architect. While your primary focus is on AI/ML innovation, you will also be responsible for building the high-performance Python backends that power these models.
We don't just "plug in" APIs; we build the engine, the fuel lines, and the dashboard. You will spend your time architecting the intelligent core, ensuring that the surrounding Python infrastructure is as sophisticated as the models it supports.

Key Responsibilities:

End-to-End System Design: Build and maintain scalable backend services in FastAPI or Flask that serve as the backbone for our Generative AI agents.
Production-Grade AI: Bridge the gap between data science and software engineering by writing clean, asynchronous Python code to handle high-concurrency AI inference.
Data & Integration: Design robust APIs and data pipelines that allow our AI models to interact seamlessly with enterprise legacy systems.
Optimized Resource Allocation: We strive to keep our talent focused on AI/ML-heavy projects; however, you will utilize your deep Python expertise to ensure the entire application stack is "AI-ready" and performant.

Required Skills:

The Python Specialist: Deep, idiomatic knowledge of Python beyond just data libraries (Asyncio, Pydantic, Design Patterns).
The Backend Architect: Proven experience building production-scale RESTful or GraphQL APIs.
Production Backends: Build highly scalable, secure, and asynchronous backend services using Django & Django REST Framework (DRF) to serve AI inferences.
The AI Strategist: Experience with LLM orchestration (LangChain/LlamaIndex) where the logic often sits at the intersection of backend code and model prompting.
Experience: minimum 3 years of professional experience in AI/ML and Backend development.
Django Mastery: Knowledge of Django. You should be a pro at ORM optimization, middleware, and building complex, secure APIs.
AI/ML Core: Proven experience with LLM integration, prompt engineering, and RAG (Retrieval-Augmented Generation) architectures.
Cloud & DevOps: Experience with Docker and deploying scalable applications on AWS, Azure, or GCP.

Good-to-Have Skills:

NLP Fundamentals: Familiarity with spaCy, Hugging Face, or NLTK is a plus but not mandatory.
MLOps: Experience with MLflow or similar tools for model versioning.
Frontend Basics: A passing familiarity with React/Vue to understand how the backend feeds the UI.