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Machine Learning Engineer
Job Description:
You will drive innovation through data engineering, machine learning and efficient deployment strategies. The ideal candidate will posses 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:
- 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.
- Good communication skills and ability to collaborate effectively in a team environment.
- Previous exposure to web or e-commerce applications and an understanding relevant industry challenges and requirements is desirable.
Note: This job description is not exhaustive. We encourage candidates to apply even if not all conditions are met, as we will provide professional growth opportunities.
Is machine learning a good career?
Yes, machine learning is a rapidly growing field and can be a very promising career option for those interested in it
As businesses and industries increasingly rely on data to drive decision-making, there is a growing need for skilled professionals who can analyze and make sense of this data
Machine learning, which involves developing algorithms that can learn from and make predictions on large datasets, is a crucial part of this process
Machine learning careers can range from data analysts, machine learning engineers, data scientists, and more
These professionals work in a variety of industries, including finance, healthcare, e-commerce, and technology
The demand for machine learning experts is high, and the salaries in this field are also generally quite competitive
However, it's important to note that machine learning can be a complex field that requires a strong background in mathematics, statistics, and computer science
It also requires ongoing learning and staying up-to-date with the latest developments and tools in the field
If you enjoy working with data, have a strong interest in programming, and are willing to put in the effort to stay current with developments, a career in machine learning can be very rewarding.