Mid-Level ML Engineer

Education and Experience »

5+ Years of Experience with a BS or MS in Computer Science or equivalent education and experience.
3+ years of experience on framework based Model development, optimization or efficient deep learning techniques.
Strong expertise in programming languages - Python, R, or Scala and technologies like Tensorflow, PyTorch, Kubernetes, Spark, Airflow, Kafka and Opensource LLMs.
Experience in working with petabyte scale data sets and developing ML frameworks in Databricks, Snowflake or similar large platforms.
Proficiency in machine learning and familiarity with open-source machine learning ecosystems.
Proficiency with CI/CD solutions in the context of MLOps and LLMOps including automation with IaC (ex: terraform).

Knowledge, Skills and Abilities »
Exceptional programming skills in Python, with popular deep learning and natural language processing (NLP) tools and libraries, including Scikit-learn, Pandas, PyTorch, TensorFlow, or other leading deep learning frameworks, NLTK and spaCy for natural language processing tasks.
Excellent communication and collaboration skills.
Strategic thinker with a strong analytical mindset and problem-solving abilities.

WHAT YOU’LL DO:
Job Responsibilities:
Build and deploy production grade Machine Learning models.
Deep technical proficiency in applying a variety of machine learning algorithms, techniques, and frameworks to solve complex business challenges.
Contribute to the continuous improvement of our machine learning systems by staying abreast of advancements in software engineering and machine learning.
Work in the full lifecycle of ML model development, from data preparation and feature engineering to model training, evaluation, and deployment.
Assemble modules into end-to-end systems, ensuring seamless integration and functionality.
Collaborate closely with Data Scientists to facilitate highly productive experimentation, model construction, and validation, fostering a sense of teamwork and shared success.
Leverage Jupyter Notebooks for prototyping, experimentation, and collaboration.