AI Engineer

Responsibilities

• Gathering, cleaning, and labeling large datasets so machine learning models can learn from them.
• Designing, training, and fine-tuning AI models (like neural networks) from scratch.
• Converting machine learning models into APIs or software so they can be seamlessly used in apps or websites.
• Connecting AI models with existing back-end/front-end systems and cloud servers.
• Evaluating the performance, speed, and accuracy of AI systems, and tweaking them to reduce errors.
• Monitoring deployed AI systems in production to ensure they adapt to new data and changing business needs.

• 3+ years of hands-on experience in developing and deploying ML models into real-world business applications or research environments.
• Strong understanding of ML/DL frameworks such as Jupyter Notebook, Anaconda, Tensor Flow, Keras, Scikit-learn, PyTorch, and MXNet.
• Proven experience working with cloud service platforms (AWS, Azure, or GCP) for ML/DL pipeline orchestration including GPU-based training (CUDA), model evaluation, and deployment (e.g., Sage Maker, Docker, or Vertex AI).
• Proficiency in Python and core data/ML libraries such as Pandas, Num Py, and Scikit-learn.
• Solid grasp of machine learning algorithms (classification, regression, clustering, feature selection, hyperparameter tuning, etc.).
• Experience developing and fine-tuning Large Language Models (LLMs) for text, code, or image generation.
• Understanding of Retrieval-Augmented Generation (RAG) architecture, including knowledge of vector databases (e.g., FAISS, ChromaDB, Milvus) and embedding models.
• Experience integrating LLMs with external tools, APIs, and data sources through frameworks such as Lang Chain, Llama Index, or similar orchestration layers.
• Familiarity with MCP (Model Context Protocol) or modern context-sharing protocols for building scalable, composable AI systems.
• Experience implementing modern AI pipelines involving fine-tuning, prompt engineering, context retrieval, and model evaluation workflows.
• Knowledge of model optimization and monitoring (latency, throughput, token efficiency, and hallucination detection).
• Ability to collaborate with cross-functional teams (data engineers, analysts, and software developers) to deliver AI-powered features and applications.