AI Automation Engineer

Responsibilities

Writing Python scripts and tools to automate data processing, reporting, and workflow tasks.
Working with data using libraries such as Pandas, NumPy, and Dask.
Integrating AI and LLM APIs such as OpenAI, Anthropic Claude, Groq, Mistral AI, Together AI, Replicate, Hugging Face, and AWS Bedrock.
Building and maintaining Python backend services using FastAPI or Flask.
Developing REST API integrations to connect internal systems, AI models, and third-party services.
Implementing prompt engineering logic, response parsing, and AI workflow handling.
Supporting the development of RAG pipelines using chunking, embeddings, vector search, and grounded response generation.
Assisting in building custom AI models or fine-tuning models based on client requirements.
Integrating third-party APIs, webhooks, and cloud services into automation workflows.
Debugging, monitoring, and improving automation tools for accuracy and performance.
Writing clean, readable, and testable code with proper error handling and documentation.
Collaborating with senior developers, AI engineers, and cross-functional teams to understand business needs and convert them into technical solutions.

Required Skills

Strong fundamentals in Python programming.
Understanding of functions, object-oriented programming (OOP), file handling, error handling, and virtual environments.
Basic knowledge of REST APIs, HTTP methods, JSON, and API integration.
Familiarity with AI/LLM concepts and integrating AI APIs.
Basic understanding of data structures and algorithms.
Experience with common Python libraries such as requests, json, os, re, csv, and argparse.
Basic knowledge of RAG, embeddings, FAISS, Pinecone, ChromaDB, or other vector databases.
Understanding of ML/DL concepts using scikit-learn, PyTorch, TensorFlow, OpenCV, NumPy, Pandas, and Matplotlib.
Basic Git knowledge, including clone, branch, commit, push, and pull request workflows.
Strong problem-solving skills and willingness to learn new technologies quickly.

Preferred Skills

Experience with FastAPI or Flask frameworks.
Exposure to prompt engineering or agent-based AI workflows.
Familiarity with cloud platforms such as AWS, Google Cloud, or Azure.
Knowledge of Docker, Redis, Celery, or asynchronous Python programming.
Basic database knowledge, including SQL, MongoDB, or SQLAlchemy.
Personal projects, internship experience, GitHub portfolio, or hackathon projects related to AI, machine learning, or automation.