AI Engineer

We are looking for a motivated AI Engineer with 1–2 years of experience to join our development team. You will assist in designing, building, and deploying AI solutions that solve real-world business problems. Your role will bridge the gap between experimental research and production-ready applications, focusing on data preprocessing, model training, and performance optimization.

Key Responsibilities

• Model Development & Training: Assist in the design and implementation of machine learning and deep learning models using frameworks like TensorFlow or PyTorch.
  
• Data Preprocessing: Clean, normalize, and augment large datasets to ensure high-quality inputs for model training.
  
• API & Service Integration: Develop and maintain REST APIs (using FastAPI or Flask) to serve AI models to end-user applications.
  
• Testing & Optimization: Conduct model evaluation and fine-tuning to improve accuracy, latency, and scalability.
  
• Collaboration: Work closely with data scientists, software engineers, and product managers to align AI features with business goals.
  
• Generative AI (Modern Requirement): Many current roles for this experience level now require hands-on experience with LLMs, prompt engineering, and RAG (Retrieval-Augmented Generation).
  
  

Required Skills and Qualifications

• Education: Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a related field.
  
• Programming: High proficiency in Python (including libraries like NumPy, Pandas, and Scikit-learn).
  
• Machine Learning Fundamentals: Solid understanding of supervised/unsupervised learning, neural networks, and evaluation metrics (e.g., F1 score, RMSE).
  
• Software Engineering: Experience with Git for version control and basic knowledge of Docker for containerization.
  
• Mathematical Foundation: Strong grasp of linear algebra, calculus, and statistics.
  
• Cloud Basics: Exposure to cloud AI services such as AWS SageMaker, Google Vertex AI, or Azure Machine Learning.
  

Preferred/Nice-to-Have Skills

• MLOps: Familiarity with MLflow or Weights & Biases for experiment tracking.
  
• Database Knowledge: Experience with SQL and NoSQL databases like PostgreSQL or MongoDB.
  
• GenAI Tools: Experience with LangChain or LlamaIndex for building LLM applications.