AI Engineer

Job overview

AI Engineers at AppStoneLab focus on building intelligent agentic workflows using large language models (LLMs) and tools like LangGraph, LangChain, and OpenAI APIs. You’ll work closely with product and engineering teams to design prompt-based systems, automate tasks using LLMs, and create scalable AI-driven solutions. The ideal candidate is curious, pragmatic, and passionate about crafting AI agents that solve real-world problems without needing complex model fine-tuning.

Responsibilities

• Take ownership of AI/ML projects from ideation to deployment
• Design and implement agentic workflows using frameworks like LangGraph and LangChain
• Build modular AI agents that can plan, reason, and execute multi-step tasks
• Create, test, and optimize structured prompts for various LLM-driven use cases
• Integrate LLMs with tools via function calling / tool usage for dynamic workflows
• Build intelligent APIs using Flask or FastAPI for production environments
• Collaborate with product, design, and engineering teams to integrate AI systems
• Work on NLP, computer vision, and recommendation-based tasks
• Analyze large datasets and perform data preprocessing and feature engineering
• Monitor and optimize performance of deployed models and agent workflows
• Continuously explore and experiment with emerging LLM capabilities, multi-agent setups, and task orchestration strategies
• Stay up to date with advancements in agentic systems, memory management, and RAG (Retrieval-Augmented Generation)

Requirements

• Bachelor's degree in Computer Science, Data Science, or a related technical field
• 0–1 years of hands-on experience in AI/ML or LLM application development
• Experience with at least one agentic AI framework (LangGraph, LangChain, AutoGen, or similar)
• Solid Python development experience
• Understanding of LLM architecture, prompt chaining, memory, and function/tool calling
• Knowledge of cloud platforms like AWS/GCP is a plus
• Experience in deploying ML models and/or AI agents into production workflows

Skills and qualifications

• Understanding of AI/ML/DL principles and practical application
• Hands-on experience with AI agent frameworks, task orchestration, and autonomous loop execution
• Knowledge of function calling, tool use, and building context-aware agents
• Exposure to multi-agent systems, memory strategies, and prompt engineering
• Familiarity with LangGraph stateful execution flows is a strong plus
• Bonus: Understanding of RAG pipelines, vector databases (e.g., Pinecone, Weaviate)
• Strong problem-solving skills and ability to think in flows rather than just models
• Good communication skills and ability to explain AI logic in product terms

Education and experience requirements

• Bachelor’s degree in Computer Science, AI, or related technical field.
• Portfolio of AI projects or contributions (GitHub, Kaggle, etc.).
• Experience with tools like TensorFlow, PyTorch, Scikit-learn.
• Familiarity with API development using Flask or FastAPI.
• Knowledge of cloud computing and deployment pipelines is a plus.

Experience

• 0 - 1 years