onsite
AI Engineer
AI Engineer
The AI Engineer will join the development team to design, build, and deploy AI solutions, focusing on data preprocessing, model training, and performance optimization. This role involves developing machine learning models, integrating them via APIs, and collaborating with cross-functional teams to achieve business goals.
About the role
About the Role
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.