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AI Engineer

icon building Company : Kannanware
icon briefcase Job Type : Full Time

Number of Applicants

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Job Description - 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). 


Requirements

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. 


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