Assist in designing, training, optimizing, and deploying machine learning and deep learning solutions. Responsibilities include data preprocessing, REST API and service integration, model evaluation, fine-tuning, scalability improvements, and collaboration with data scientists, software engineers, and product managers. The role also involves generative AI work with LLMs, prompt engineering, and retrieval-augmented generation.
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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