Key Responsibilities
- Design, build, and evaluate machine learning and deep learning models for internal products and client solutions.
- Perform data collection, cleaning, preprocessing, feature engineering, and exploratory data analysis.
- Develop and fine-tune components for GenAI/LLM-based solutions and multi-agent AI systems.
- Write clean, efficient, well-documented, and testable code for model development and data pipelines.
- Support model deployment, monitoring, and performance tuning in collaboration with engineering (MLOps practices).
- Validate model outputs, run experiments, and document methodology and results for reproducibility.
- Collaborate with senior data scientists and engineers on live client projects across multiple industries.
- Research and evaluate new ML/AI techniques, frameworks, and tools, and recommend improvements to existing models and pipelines.
- Participate in code reviews, sprint planning, and team knowledge-sharing sessions.

Required Skills & Qualifications
- Bachelor's/Master's degree in Computer Science, Data Science, Statistics,



Mathematics, or a related field.
- 0-1 year of hands-on experience in machine learning through internships, academic projects, research, or a full-time role.
- Strong proficiency in Python and familiarity with core data structures and algorithms.
- Practical experience with ML libraries such as NumPy, Pandas, and scikit-learn, and building models (regression, classification, clustering).
- Working knowledge of a deep learning framework (TensorFlow or PyTorch) through coursework, projects, or internships.
- Solid understanding of statistics and probability fundamentals.
- Familiarity with SQL and working with structured/unstructured data.
- Exposure to Generative AI/LLM concepts (e.g., prompt engineering, RAG, transformers, embeddings) is a solid plus.
- Basic understanding of version control (Git) and software engineering best practices.
- Strong analytical and problem-solving skills, with a proacti

📌 Machine Learning Engineer (Bengaluru)
🏢 Predictive Research
📍 Bengaluru