Collaborate with cross-functional teams to understand business requirements and design to productionize machine learning, ML ops, traditional AI, LLMs, and agentic AI solutions into practical product-driven applications.
Design and implement application integrations to leverage newly built machine learning, ML ops, traditional AI, LLMs, and agentic AI products.
Build solutions that improve delivery speed and scalability of product pipelines.
Leverage managed and serverless cloud offerings to create performant and scalable cloud-native application solutions and data pipelines.
Design and implement advanced machine learning models using techniques like regression, classification, Bayesian methods, and Retrieval-Augmented Generation (RAG).
Develop and deploy Agentic AI prototypes and other advanced machine learning solutions.
Work closely with GenAI engineers to integrate prompt engineering and RAG components into production systems.
Participate in customer engagement activities, clearly communicating technical concepts and project progress to stakeholders.

Bachelor's degree or higher in Computer Science or a related field.
5+ years of industry experience as a hands-on practitioner in ML/AI, Data Science, or Data Engineering.
Proven track record of successful delivery of ML/AI and data projects.
Experience with NLP and LLM-based technologies and frameworks.
Proficiency in programming languages such as Python and SQL (JavaScript a plus).
Understanding and passion for Test Driven Design.
Familiarity with ETL/ELT concepts and best practices.
Experience with prompt engineering and RAG techniques a plus.
Expertise in 2–3 major cloud and data platforms (e.g., AWS, GCP, Azure, Snowflake, or Databricks).
Experience with Docker a plus.
Effective communication skills and the ability to collaborate effectively with both technical and non-technical stakeholders.

Mid-Level Machine Learning Engineer

Key skills