Senior ML Engineer

Job Summary

Looking for a culture to thrive & build a rewarding career for yourself, join the core team of young hustlers building the next generation of Machine Learning platform & services. You will develop training and deployment pipelines for machine learning, implement model compression algorithms, and productionize machine learning research solving challenging business problems.

Looking for immediate joiners!

Key Responsibilities:

• Create & maintain machine learning pipelines and workflow orchestration. Take development projects to production and implement CI/CD pipelines.
• Continuously monitor the performance of machine learning implementations and identify opportunities for improvement.
• Establish and maintain data pipelines. Ensure operational scalability.
• Develop and deploy scalable tools and services for our clients to handle machine learning training and inference.
• Apply software engineering rigor and best practices to machine learning pipelines.
• Identify and evaluate new technologies to improve performance, maintainability, and reliability of machine learning solutions.
• Work across teams to bring continuous improvement to engineering processes and tools.
• Facilitate the development and deployment of proof-of-concept machine learning systems.
• Lead the development and fine-tuning of large language models to achieve optimal performance across a range of tasks and domains.

Key Qualifications:

• Degree in computer science, software engineering, mathematics, or a related field.
• At least 3 years of experience in software engineering, and a strong understanding of model deployment and monitoring.
• Experience building data pipelines, deploying machine learning models in production, and monitoring and maintaining their performance. Experience embedding monitoring solutions in ML applications.
• Good to have the knowledge of LLM.
• Strong programming skills in python. Experience with automation for deploying machine learning solutions using python/bash scripting.
• Extensive experience with Git, Docker, Kubernetes, and other DevOps tools and a good understanding of Linux for managing servers.
• Experience with cloud-based ecosystems, especially AWS ML services. Experience with AWS SageMaker highly preferred.
• Good to have experience in developing, fine-tuning, and optimizing large language models, such as GPT-3, BERT, or similar.
• Exposure to machine learning frameworks and tools.
• Excellent communication and collaboration skills.
• Ability to work independently and in a team-oriented environment.
• Methodical and meticulous towards work and planning.