Machine Learning Engineer
Job Description:
You will drive innovation through data engineering, machine learning and efficient deployment strategies. The ideal candidate will possess a robust comprehension of ML principles and their scientific underpinnings while seamlessly applying this knowledge within an engineering and product-focused environment.
Responsibilities:
Data Engineering: Design and develop robust data pipelines for acquiring, preprocessing, and transforming diverse datasets to support machine learning models. Implement scalable solutions for data ingestion, storage, and retrieval.
Machine Learning Development: Utilize state-of-the-art machine learning techniques to build predictive, generative models and recommendation systems. Focus on Natural Language Processing (NLP), including large language models (LLM). It's nice to have multi-modal capabilities and proficiency in Computer Vision techniques.
Model Deployment & Evaluation: Implement efficient and scalable deployment pipelines for machine learning models, ensuring seamless integration into production environments. Collaborate with DevOps and software engineering teams to automate deployment processes and monitor and evaluate model performance in real time.
Continuous Improvement: Stay updated with the latest advancements in the ML space. Proactively identify opportunities to enhance existing models and pipelines, driving innovation and efficiency.
Requirements:
- 3-5 years of experience of commercial environment experience.
- Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or related field.
- Strong understanding of software engineering best practices, version control systems, CI/CD, and agile development methodologies.
Proven experience in data engineering, including data acquisition, preprocessing, and ETL. - Proficiency in programming languages such as Python, with experience in ML frameworks like PyTorch, TensorFlow and libraries like HuggingFace, Pandas, Bokeh.
- Experience designing, training, and deploying machine learning models in production environments encompassing containerization technologies like Docker, cloud platforms, and model-serving frameworks like TorchServe and MLFlow. Scaling strategy experience in a high-throughput, low-latency scenario is desirable. Additionally, familiarity with advanced DevOps capabilities, such as Kubernetes, is nice to have.
- Previous exposure to web or e-commerce applications and an understanding relevant industry challenges and requirements is desirable.
