AI Engineer

Role Overview

As an AI Engineer at Nineleaps, you will work on building, testing, and improving machine learning solutions that solve real-world business problems. You'll contribute across the AI lifecycle—from data preparation and model experimentation to evaluation, documentation, and collaboration with cross-functional teams. This role is ideal for professionals with a strong foundation in machine learning and Python who are excited to explore modern AI concepts, including deep learning, LLMs, and generative AI.

Key Responsibilities

• Implement, test, and optimize foundational machine learning models using frameworks such as TensorFlow, PyTorch, or scikit-learn
• Assist with data collection, preprocessing, and exploratory data analysis (EDA) to prepare high-quality datasets for model training and evaluation
• Conduct literature reviews and stay up to date with the latest developments in AI, machine learning, and generative AI
• Apply new ideas and emerging techniques in prototypes and proof-of-concept solutions
• Document technical processes, experiments, and results thoroughly to support reproducibility and knowledge sharing
• Participate actively in code reviews, brainstorming sessions, and team discussions to foster collaboration and innovation
• Present findings, analyses, and model outcomes clearly to both technical and non-technical stakeholders
• Support experimentation and evaluation of models to improve accuracy, efficiency, and business relevance

What We're Looking For

• Strong proficiency in Python and hands-on experience with core libraries such as NumPy and pandas
• Foundational experience with at least one major deep learning framework, such as TensorFlow or PyTorch, along with scikit-learn for classical machine learning tasks
• Strong grasp of data structures and algorithms (DSA) and core computer science fundamentals
• Familiarity with version control tools such as Git
• Solid understanding of core AI/ML concepts, including supervised learning, unsupervised learning, classification, regression, and model evaluation metrics
• Understanding of deep learning concepts and architectures, especially Transformer models, Large Language Models (LLMs), and generative AI
• Familiarity with data preprocessing, feature selection, and basic data visualization techniques
• Basic conceptual understanding of RAG (Retrieval-Augmented Generation) architectures and prompt engineering techniques
• Strong analytical thinking, attention to detail, and a proactive problem-solving mindset
• Excellent written and verbal communication skills
• Prior internships, academic ... [truncated for excel]