AI/ML Engineer

AI / ML Engineer

About The Role

This position is part of our newly forming product team, where you will contribute to building the product’s AI and machine learning capabilities from the ground up. You will take part in designing and experimenting with models in areas such as LLMs, anomaly detection, forecasting, and time-series analysis, working with real-world data and practical use cases. You will work in a hands-on environment that values problem-solving, continuous learning, and a passion for new technologies, with a strong focus on building solutions that provide tangible value.

What You Will Do

• Develop and experiment with AI models in LLM, NLP, anomaly detection, and time-series forecasting
• Work on data collection, cleaning, preprocessing, and feature engineering for real-world structured and unstructured datasets
• Build and evaluate models using classical machine learning and modern deep learning frameworks
• Continuously monitor, analyze, and improve model performance for accuracy, scalability, and reliability
• Deploy AI models into production environments using FastAPI, Docker, and related tools
• Collaborate closely with the team to contribute ideas, influence architectural decisions, and accelerate product development

What We're Looking For

• Bachelor's degree in Computer Engineering, Software Engineering, AI/Data Science, or equivalent practical experience
• Proficiency in Python and core machine learning libraries (pandas, NumPy, scikit-learn)
• Hands-on exposure to LLMs, embeddings, forecasting, or anomaly detection, with a strong desire to build real-world AI solutions in these areas
• Familiarity or experience with time-series modeling techniques (ARIMA, Prophet, LSTM, Transformer-based models)
• Basic experience with PyTorch, TensorFlow, etc.
• Experience with backend frameworks such as FastAPI or similar technologies
• Strong analytical and problem-solving abilities
• Good command of English for technical communication
• Curiosity, adaptability, and a growth mindset suitable for an innovation-focused startup

Nice to Have

• Knowledge of classical machine learning algorithms, statistical modeling, time-series forecasting methods, and optimization techniques (e.g., regression models, clustering, gradient boosting, ARIMA, LSTM, etc.)
• Experience with vector databases, RAG pipelines, or time-series forecasting frameworks
• Familiarity with Docker or container technologies
• Exposure to model serving tools (Ollama, vLLM, etc.)
• Interest in agent-based AI architectures and workflow automation tools (LangGraph, CrewAI, etc.)
• Participation in Kaggle competitions, AI hackathons, research projects, or open-source contributions