Description

Key Skills: Data Engineer, Data Engineering, GCP

Roles and Responsibilities:

Architecture & Core Engineering

• Data & AI Infrastructure: Design, implement, and maintain scalable, fault-tolerant data pipelines (ETL/ELT) and MLOps frameworks to support real-time and batch processing.
• AI Integration: Architect and deploy production-grade ML models, Generative AI applications, and Retrieval-Augmented Generation (RAG) systems.
• Data Modeling: Establish best practices for data warehousing, lakehouses, and vector databases to support both analytical and operational AI needs.
• Performance Tuning: Optimize distributed computing workloads and model inference costs for high throughput and low latency.

2. Leadership & Strategy

• Team Mentorship: Guide, code-review, and mentor a team of data and ML engineers, fostering a culture of technical excellence and continuous learning.
• AI Roadmap: Partner with leadership to define the company's AI capabilities, evaluating new frameworks, tools, and SaaS providers.
• Governance & Compliance: Define data governance, security policies, and ethical AI guidelines (e.g., data privacy, bias mitigation, model drift tracking).

Skills Required:

• 6+ years of professional experience in data engineering or software engineering.
• 2+ years of experience leading technical teams or acting as a principal/lead engineer.
• Proven track record of taking AI/ML models out of notebooks and successfully embedding them into production-grade software.
• Programming: Mastery of Python and solid proficiency in SQL, Scala, or Java.
• Data Engineering: Deep experience with modern data stack tools like Spark, Databricks, Snowflake, dbt, and orchestrators like Apache Airflow.
• AI & Machine Learning: Practical experience deploying frameworks like PyTorch or TensorFlow, alongside GenAI tools like LangChain, LlamaIndex, and vector databases (e.g., Pinecone, Milvus, or Chroma).
• Cloud & DevOps: Strong hands-on experience with cloud platforms (AWS/Azure/GCP) and containerization (Docker, Kubernetes), specifically tailored for MLOps

Education: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field

Key Skills

Education

Any Graduate