Offer summary

Qualifications:

5+ years of experience in software or data engineering with a focus on scalable solutions., 1+ year of experience in deploying and managing ML models., Strong programming skills in Python and experience with ML platforms like Airflow or similar., Knowledge of API development and proficiency in using Git for version control..

Key responsabilities:

Develop and refine ML engineering platforms and components to handle large data volumes.

Establish processes for model management, including data preparation and prediction.

Collaborate with client-facing teams to translate requirements into features and provide technical support.

Write robust, maintainable code and participate in code reviews to ensure high standards.

Job description

Intellectsoft is a software development company delivering innovative solutions since 2007. We operate across North America, Latin America, the Nordic region, the UK, and Europe.We specialize in industries like Fintech, Healthcare, EdTech, Construction, Hospitality, and more, partnering with startups, mid-sized businesses, and Fortune 500 companies to drive growth and scalability. Our clients include Jaguar Motors, Universal Pictures, Harley-Davidson, Qualcomm, and London Stock Exchange.Together, our team delivers solutions that make a difference. Learn more at www.intellectsoft.net

Our customer's product is an AI-powered platform that helps businesses make better decisions and work more efficiently. It uses advanced analytics and machine learning to analyze large amounts of data and provide useful insights and predictions. The platform is widely used in various industries, including healthcare, to optimize processes, improve customer experiences, and support innovation. It integrates easily with existing systems, making it easier for teams to make quick, data-driven decisions to deliver cutting-edge solutions.

Requirements

5+ years of experience in software or data engineering, encompassing system design, development, and deployment of scalable solutions in production environments.
1+ year of experience in deploying and managing ML models.
Proficiency with ML Ops for assessing and monitoring model performance and scalability.
Skilled in creating feature engineering processes and inference pipelines.
Strong programming skills in Python.
Experience with ML platforms like or similar to Airflow or any other orchestration workflow framework (SageMaker, Kubeflow, MLFlow).
Experience with Test-Driven Development (TDD), including writing unit tests with Pytest and implementing integration tests to ensure robustness and reliability.
Experience with DevOps concepts, CI/CD pipelines, and data security measures, along with expertise in cloud platform architecture.
Knowledge and experience in API development, including FastAPI and REST API.
Proficiency in using Git for version control.
Ability to write clean code and well-structured Pull Requests.
Hands-on experience in data engineering within Big Data ecosystems.
Knowledge of fundamental computer science concepts, including common data structures and algorithms.
Ability to collaborate effectively with diverse teams.
Excellent English language skills.

Nice to have skills

Experience with distributed computing frameworks like Spark (PySpark).
Proficiency in deploying models on cloud platforms such as AWS, Azure, or GCP. Experience with Kubernetes would be a big plus.
Experience with Large Language Models (LLMs) development and fine-tuning.
Familiarity with Retrieval-Augmented Generation (RAG) pipelines.

Responsibilities

Develop, refine, and use ML engineering platforms and components.
Ensure our programs can efficiently handle large volumes of data and meet deadlines.
Establish and manage processes for models, including data preparation and prediction.
Monitor model performance closely and address any issues promptly.
Collaborate closely with client-facing teams to understand their needs and provide technical support.
Translate client requirements into straightforward features.
Write robust code that is easy to test, maintain, and troubleshoot.
Maintain high standards by adhering to guidelines, participating in code reviews, and ensuring code quality.
Thoroughly test all components to anticipate and resolve potential issues.
Utilize tools for issue tracking, code review, and version control.
Actively participate in team meetings to discuss progress and future plans.
Stay updated on the latest developments in technology and explore innovative solutions.