Junior AI Engineer (Generative AI & Machine Learning)

About The Role

• Develop, fine-tune, and integrate LLM-based systems using platforms such as OpenAI, Gemini, and Hugging Face Transformers
• Build and deploy AI microservices using FastAPI and Python
• Design and prototype AI agents, prompt orchestration pipelines, and reasoning workflows (e.g., LangChain, OpenDevin, CrewAI, or custom agents)
• Apply classical machine learning (classification, regression, clustering, feature extraction) where appropriate
• Experiment with model evaluation, embeddings, and retrieval-augmented generation (RAG)
• Collaborate with full stack teams to integrate AI features into production environments
• Manage APIs, containers, and data pipelines for deploying scalable AI applications
• Write clean, modular, and well-documented code
  

Required Skills & Experience

• 1–3 years of experience in Python with a focus on AI/ML projects
• Strong knowledge of transformer models and experience using Hugging Face libraries
• Understanding of prompt engineering, LLM evaluation, and function calling / tool use
• Hands-on experience with FastAPI or Flask for serving ML models
• Familiarity with LangChain, LLM agents, or multi-agent orchestration frameworks
• Solid grasp of machine learning fundamentals (data preprocessing, model training, evaluation metrics)
• Experience with NumPy, pandas, scikit-learn, and at least one deep learning framework (PyTorch or TensorFlow)
• Exposure to cloud environments like GCP or AWS for deploying AI workloads
• Good understanding of Git, Docker, and API integration
  

• Experience with vector databases (e.g., Pinecone, Chroma, Weaviate, FAISS)
• Knowledge of RAG pipelines and LLM tool/function calling
• Experience integrating AI models into Node.js/Vue.js based full stack applications
• Basic understanding of data pipelines, ETL, or MLOps workflows
• Awareness of ethical AI and model interpretability best practices
  

• Work on real-world Generative AI systems and AI agent orchestration projects
• Mentorship from senior AI engineers and applied researchers
• Access to GPU infrastructure and cloud AI tooling
• Opportunity to work across the entire AI lifecycle — from ideation to production deployment
• Competitive compensation and opportunities for rapid growth