Senior ML Engineer

As a Senior ML Engineer at Intellectsoft, your main responsibilities will include designing and implementing ML models for various applications such as customer segmentation and trend forecasting. You'll also be tasked with developing and optimizing data pipelines for feature extraction, deploying models using MLflow and FastAPI, and monitoring model performance. Your role will involve automating retraining pipelines and working with big data frameworks like Apache Spark for scalable ML processing.

To qualify for the Senior ML Engineer position at Intellectsoft, candidates should have a minimum of 7 years of overall engineering experience, with at least 4 years specifically in machine learning and AI. A strong foundation in Python, PyTorch, TensorFlow, and MLflow is essential. Experience with MLOps, CI/CD for ML models, and proficiency in SQL and feature engineering techniques are highly desirable, along with hands-on experience in distributed computing using Apache Spark.

Intellectsoft offers an array of benefits for Senior ML Engineers, including 35 absence days per year to promote work-life balance. Employees have access to personal development opportunities such as Udemy courses of their choice, English language courses with native speakers, and regular soft-skill training sessions. Team-building activities, both online and offline, are also a part of our engaging work culture!

At Intellectsoft, we prioritize the professional growth of our Senior ML Engineers by providing access to various learning resources, including Udemy courses tailored to your interests. We also organize excellence center meetups and regular training sessions focused on enhancing soft skills. This commitment to continuous learning ensures that our team remains at the forefront of the latest in machine learning and AI innovations.

As a Senior ML Engineer at Intellectsoft, you'll have the opportunity to work with a variety of advanced technologies and tools. You'll utilize Python along with libraries like PyTorch and TensorFlow for developing ML models. Additionally, experience with MLOps practices and tools like MLflow and FastAPI will be central to your role. Your work will also include big data technologies such as Apache Spark and SQL databases, enabling you to handle large datasets effectively.

In answering this question, discuss specific projects where you utilized TensorFlow or PyTorch. Highlight how you implemented models, trained them, and fine-tuned parameters for optimal performance, showcasing your hands-on experience and understanding of these frameworks.

When answering, explain that MLOps combines machine learning and DevOps practices to streamline the deployment and maintenance of ML models. Discuss the importance of automation, continuous integration, and continuous delivery in this context, and share any relevant experiences you have working in MLOps.

Provide a structured response describing your data preprocessing steps, including data cleaning, normalization, and feature selection. Mention any tools you use and how these steps contribute to building effective ML models, ensuring the audience understands your methodology.

In your response, emphasize the importance of monitoring metrics specific to your model's purpose, such as accuracy, precision, and recall. Discuss tools or frameworks you use for monitoring and mention methods for automating model retraining in production, showing your understanding of the ML lifecycle.

Discuss what feature engineering entails, including techniques like creating new features from existing data, handling missing values, and selecting the most relevant features for model training. Highlight its critical role in improving model accuracy and how your efforts in this area have led to better outcomes in previous projects.

When discussing a challenging project, focus on the issue at hand, the approach you took to resolve it, and the outcome. Detail specific technical problems and how you leveraged your skills to find a solution, showcasing your problem-solving abilities and resilience.

Illustrate your communication skills by explaining a complex concept in simple terms, perhaps by using metaphors or relatable examples. Emphasize the importance of clear communication in a cross-functional team environment and any experiences you've had doing this previously.

In your answer, explain how SQL is fundamental for querying and managing datasets, especially in large-scale projects. Highlight your experience writing queries to extract, manipulate, and analyze data, emphasizing how these skills enhance the effectiveness of your ML workflows.

Describe your routine for staying informed about advancements in the field, such as following reputable blogs, attending conferences, or participating in online courses. Talk about how this commitment to learning helps you innovate and improve your contributions as a Senior ML Engineer at Intellectsoft.

Your answer should reflect your enthusiasm for Intellectsoft's innovative culture and projects, along with your alignment with the company’s mission. Mention specific reasons such as the opportunity to work with prestigious clients or the supportive working environment that encourages professional growth.