Platform Engineer III

• *Job Summary**
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As the **Data \& AI Lead Engineer**, you will bridge the gap between complex data engineering and cutting\-edge artificial intelligence. You will architect, build, and scale robust data pipelines while leading the integration of Machine Learning (ML) and Large Language Model (LLM) workflows into our core products

• *Key Responsibilities**
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• *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).

• *Skill Requirements**
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• **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

• *Must Have Skill**
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Google Cloud Platform (GCP)

• *Good to Have Skill**
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• *Other Requirements**
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• **Bachelor’s or Master’s degree** in Computer Science, Data Science, Engineering, or a related quantitative field.
• **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.