Senior AI Engineer

Location Name:

Job Purpose

Duties And Responsibilities

• Perform root cause analysis (RCA) on model performance, data issues, and system
  

• Write clean, efficient, and scalable Python code for AI/ML applications.
• Build, document, and maintain backend APIs using FastAPI to support real-time and
  

• Apply machine learning and deep learning techniques for text classification, image
  

• Develop and integrate NLP models for tasks such as sentiment analysis,
  

• Use YOLO, OpenCV, and OCR libraries (e.g., Tesseract, EasyOCR) for computer vision
  

• Leverage Generative AI models (e.g., OpenAI GPT, Google Gemini) to create
  

• Manipulate and visualize data using NumPy, Pandas, Matplotlib, and Seaborn.
• Work with NoSQL databases (e.g., MongoDB, Redis) for scalable data storage and
  

• Operate and deploy solutions effectively in Linux-based environments.
• Participate in code reviews, debugging, and performance optimization.
• Follow through on assigned action items, timelines, and deliverables in an Agile
  

Required Qualifications And Experience

• Strong experience in Python programming and development in Linux environments.
• Solid background in data engineering and ETL pipeline design.
• Hands-on experience with ML/DL frameworks (e.g., TensorFlow, PyTorch, Scikitlearn).
• Proven experience in NLP tasks and classification problems (text/image).
• Experience with YOLO, OCR, and OpenCV for computer vision projects.
• Knowledge of Generative AI models like GPT, Gemini, Claude, or LLaMA.
• Strong problem-solving and analytical skills; ability to work independently on
  

• Skilled in building and maintaining FastAPI-based APIs and services.
• Proficiency with data manipulation and visualization tools (NumPy, Pandas,
  

• Experience integrating with NoSQL databases.
• Excellent multitasking and project follow-through capabilities in a fast-paced team
  

• Familiarity with Git, VS Code, and modern development practices.
  

• Production experience deploying AI/ML models in cloud or on-prem environments.
• Experience with MLOps practices, model versioning, and monitoring.
• Familiarity with containerized deployment using Docker and/or Kubernetes.
• Experience working in a POD delivery model or Agile squads.