Principal Architect

Role Overview

• Play a pivotal role in building and scaling machine learning models from development to production.
• Create efficient and reliable ML pipelines.
• Lead the end-to-end design, development, and delivery of ML and GenAI solutions.
• Design and implement advanced data pipelines and AI systems, including batch and streaming data processing.
• Build and optimize robust data foundations for AI by developing high-quality, scalable ETL/ELT pipelines.
• Define and institutionalize evaluation, validation, and governance frameworks for ML/GenAI systems.
• Partner with business stakeholders to translate objectives into data-driven AI/ML solutions.
• Establish and enforce best practices in MLOps, LLMOps, DataOps, and DevOps.
• Architect and oversee scalable cloud-based data and AI platforms.
• Drive experimentation strategy, including A/B testing and prompt optimization.
• Provide mentorship to L4 and L5 engineers in data engineering and AI/ML development.
• Lead cross-functional collaboration across data engineering, data science, platform engineering, and business teams to deliver integrated AI solutions.
• Stay at the forefront of advancements in data engineering and Generative AI.

Requirements

• Doctorate degree and 2 years of experience OR Master’s degree and 4 years of experience OR Bachelor’s degree and 6 years of experience OR Associate’s degree and 10 years of experience OR High school diploma / GED and 12 years of experience
• Deep expertise in machine learning, deep learning, and Generative AI (LLMs, transformers, embeddings, fine-tuning techniques).
• Proven track record of leading and delivering production-grade ML/GenAI systems end-to-end with measurable business impact with strong experience in designing scalable system architectures for ML and GenAI, including distributed systems and high-throughput pipelines.
• Expertise in MLOps/LLMOps ecosystems (MLflow, Kubeflow, Airflow, CI/CD, Docker, Kubernetes).
• Strong system design, architecture, and problem-solving skills with the ability to operate independently and lead large initiatives.
• Demonstrated proficiency in leveraging cloud platforms (AWS, Azure, GCP) for data engineering solutions.
• Strong understanding of cloud architecture principles and cost optimization strategies.
• Proven ability to mentor and guide junior and mid-level engineers (L4/L5).
• Good-to-Have Skills: Cloud certifications (AWS, Azure, or GCP) are a plus
• Strong experience with big data ecosystems , including Apache Spark, Hadoop , and large-scale distributed data processing
• Deep expertise in data engineering , including building and optimizing scalable data pipelines and platforms using Databricks, Spark, SQL, and Python
• Advanced proficiency in Python and modern ML/AI frameworks (e.g., PyTorch, TensorFlow, Hugging Face, LangChain, or similar)
• Experience designing robust evaluation and validation frameworks , including automated evaluations, human-in-the-loop systems, safety testing, and monitoring
• Extensive experience with Retrieval-Augmented Generation (RAG) architectures, vector databases , and knowledge-grounded AI systems
• Strong understanding of agentic AI frameworks , including orchestration, planning, memory management, and tool integration
• Solid foundation in statistical modeling, experimentation design (A/B testing), and causal inference
• Experience with NLP, semantic search, embeddings, and vector search systems
• Familiarity with Responsible AI practices , including fairness, explainability, governance, and regulatory compliance
• Hands-on experience with cloud-native AI/ML services across AWS, Azure, or GCP , including cost and performance optimization
• Experience with the Databricks Lakehouse platform for enterprise-scale data engineering, ML, and GenAI workloads
• Exposure to advanced evaluation techniques such as red-teaming, adversarial testing, and synthetic data generation
• Strong experience in data modeling and performance tuning for both OLAP and OLTP systems
• Hands-on experience with workflow orchestration tools such as Apache Airflow , and distributed processing frameworks like Apache Spark

Tech Stack

• Airflow
• Apache
• AWS
• Azure
• Cloud
• Distributed Systems
• Docker
• ETL
• Google Cloud Platform
• Hadoop
• Kubernetes
• Python
• PyTorch
• Spark
• SQL
• Tensorflow

Benefits

• A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions
• group medical, dental and vision coverage
• life and disability insurance
• flexible spending accounts
• A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
• Stock-based long-term incentives
• Award-winning time-off plans
• Flexible work models where possible.