ML Engineer

A leading Gaming Company is looking for a Machine Learning Engineer to join its team and play a critical role in building, deploying, and maintaining machine learning models at scale. 

Responsibilities:

• Model Deployment & Productionization: Work with data scientists to take machine learning models from research to production, ensuring scalability, performance, and reliability.

• ML Infrastructure & Pipelines: Build and maintain end-to-end ML pipelines, including data preprocessing, feature engineering, model training, and monitoring.

• Software Engineering Best Practices: Write high-quality, well-documented, and efficient Python code for ML applications, ensuring proper version control and CI/CD integration.

• Performance Optimization: Optimize model inference time, data processing efficiency, and infrastructure cost-effectiveness.

• Monitoring & Maintenance: Implement model monitoring strategies to detect model drift, ensure high performance, and maintain model explainability.

• Collaboration Across Teams: Work closely with data scientists, data engineers, and business teams to understand problems, define requirements, and deliver impactful ML solutions.

•  2-4 years of experience in machine learning engineering, software engineering, data engineering, or any related field.

• Bachelor’s or master’s degree in computer science, Data Science, Artificial Intelligence, or a related discipline.

• Strong proficiency in Python, including experience with Python software development, Python-based ML libraries, and API development (FastAPI).

• Deep understanding of Python programming principles, including object-oriented programming, asynchronous processing, and memory management.

• Solid software engineering skills, including experience with Git, Docker, and CI/CD pipelines.

• Experience with big data tools (Spark, Hadoop) and working with structured and unstructured data.

• Experience with real-time ML applications and streaming data (Kafka, Flink, etc.).

• Familiarity with DevOps and infrastructure as code (Kubernetes).

• Experience in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI Agents using frameworks like LangChain and LlamaIndex – Advantage.

• Hands-on experience deploying models using tools like Kubernetes or BentoML – Advantage.