Role Overview
This position is for a Principal Engineer who will lead the design and implementation of advanced data science and machine learning solutions in cloud environments. The role centers on transforming business challenges into robust technical architectures, ensuring alignment with scalability, security, and performance standards. You will guide development teams through architectural decisions, enforce best practices, and drive innovation across the full lifecycle of data-driven systems.
Key Responsibilities
- Interpret client business objectives and technical constraints to develop effective, elegant system designs.
- Translate high-level requirements into actionable technical direction for engineering teams.
- Evaluate multiple technical approaches and recommend optimal solutions based on client needs.
- Establish benchmarks and guidelines for non-functional requirements during development cycles.
- Produce and review architectural documentation covering system frameworks, design patterns, and integration strategies.
- Conduct thorough reviews of system designs with attention to extensibility, security, performance, and user experience.
- Define and oversee the implementation of end-to-end solutions for both functional and non-functional goals.
- Apply insights from past integration projects to improve current system designs.
- Diagnose and resolve complex technical issues through root cause analysis and evidence-based decision-making.
- Lead proof-of-concept initiatives to validate proposed technologies and architectural choices.
Required Qualifications
- Minimum of 11 years in technical roles with a focus on data science and systems architecture.
- Proven experience in predictive modeling techniques including statistical analysis, regression, ANOVA, and time series forecasting.
- Strong background in deploying data science solutions on Google Cloud Platform, with foundational knowledge of Generative AI.
- Extensive programming experience in Python or R, including libraries such as Pandas, NumPy, and Scikit-learn.
- Deep understanding of cloud infrastructure and deploying scalable data systems across AWS, Azure, or GCP.
- Track record in machine learning, natural language processing, and optimization methods such as linear programming, integer programming, genetic algorithms, and reinforcement learning.
- Hands-on experience with Retrieval-Augmented Generation (RAG), Lang Chain, Llama Index, and prompt engineering techniques.
- Familiarity with MLOps practices, including CI/CD pipelines, model monitoring, and lifecycle management.
- Strong communication skills and a collaborative approach when working with interdisciplinary teams.
