Machine Learning Engineer

Key Responsibilities: Design, develop, and deploy machine learning algorithms and models to improve product functionality and performance. Collaborate with cross-functional teams to translate business requirements into scalable technical solutions. Conduct research on emerging machine learning techniques and integrate cutting-edge advancements into products. Design, fine-tune, and customize LLMs and SLMs on domain-specific datasets to solve targeted business problems. Implement efficient model training techniques such as Parameter-Efficient Fine-Tuning (PEFT) methods, including LoRA. Apply optimization strategies like quantization, pruning, and knowledge distillation to enhance deployment efficiency and reduce latency. Build and maintain robust data processing, training, and evaluation pipelines for generative AI workflows. Work closely with product managers, designers, and engineering teams to deliver high-quality features within timelines. Participate in peer code reviews to maintain best practices, clean architecture, and performance standards. Troubleshoot, debug, and resolve model-related or algorithmic issues during development and deployment. Document technical designs, model configurations, and development workflows for team and stakeholder alignment. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. 3–5 years of experience developing and deploying machine learning models in production environments. Strong understanding of supervised learning, unsupervised learning, deep learning, and natural language processing. Hands-on experience fine-tuning and customizing language models (LLMs/SLMs), even for less than a year in this emerging domain. Practical experience with the Hugging Face ecosystem (Transformers, PEFT, Datasets). Proficiency in Python and Java programming languages. Strong analytical thinking, problem-solving, and debugging skills. Ability to thrive in a fast-paced, dynamic, and collaborative environment.