- Company Name
- Qodea
- Job Title
- Principal Machine Learning Engineer
- Job Description
-
**Job Title:** Principal Machine Learning Engineer
**Role Summary:**
Design, architect, and lead the development of scalable data pipelines and machine learning systems at enterprise scale. Drive innovation in data quality, enrichment, and semantic linking to extract actionable insights and maintain high reliability, performance, and observability of data services.
**Expactations:**
- Deliver robust, production‑grade ML and data solutions that meet stringent quality and performance SLAs.
- Lead cross‑functional teams, align stakeholders, and mentor engineers to uphold best practices.
- Continuously evaluate and apply emerging AI/ML technologies (semantic understanding, NLP, LLMs) to improve data pipelines and enrichment.
- Maintain ownership of technical strategy and road‑map for data & ML services within the domain.
**Key Responsibilities:**
- Architect and evolve distributed data pipelines (ETL/ELT) for ingestion, transformation, quality checks, and enrichment.
- Mentor and guide ML engineers, data scientists, and infrastructure engineers, fostering a culture of code quality and operational excellence.
- Integrate modern ML/AI tools (e.g., semantic models, LLMs) to automate data quality checks and canonical product linking.
- Define and enforce metrics for data quality and model effectiveness, including offline and online validation frameworks.
- Champion engineering best practices: CI/CD, observability, security, and data governance across teams.
- Stay current on AI/ML, data engineering, and distributed system trends; translate insights into production solutions.
**Required Skills:**
- 7+ years of experience designing and leading large‑scale distributed data and ML backend systems.
- Deep expertise in ETL/ELT pipeline design and optimization.
- Proficiency in Apache Beam, Pub/Sub, Redis, and large‑scale processing frameworks.
- Strong backend development skills in Python and Scala; experience with Node.js or Go is a plus.
- Advanced knowledge of SQL and NoSQL databases; data warehousing experience.
- Proven ability to build robust APIs (REST, GraphQL) and deploy in cloud environments (GCP preferred).
- Experience with Kubernetes, Docker, CI/CD pipelines, and observability tooling.
- Excellent communication skills; ability to influence cross‑functional technical direction.
**Required Education & Certifications:**
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (advanced degrees preferred).
- Relevant certifications (e.g., GCP Professional Data Engineer, Machine Learning Engineer, or equivalent) are advantageous.