Senior AI/ML Architect, Applied Field Engineering

Required

• Python Programming

  Expert-level Python development with strong software engineering practices, code quality standards, and ability to write scalable ML pipelines and data processing applications.

• Machine Learning Engineering

  End-to-end ML expertise including problem formulation, feature engineering, model training, evaluation, optimization, and production deployment. Deep understanding of ML workflows, experimentation frameworks, and MLOps practices.

• Generative AI & LLM Applications

  Practical expertise building applications with Large Language Models including prompt optimization, RAG systems, fine-tuning strategies, and evaluation metrics for generative AI outputs in production environments.

• Data Engineering & SQL

  Strong SQL skills and familiarity with modern data engineering practices including data pipelines, transformations, orchestration, and integration of data with ML workflows. Understanding of data quality, governance, and lineage.

• Cloud Platform Expertise

  Hands-on experience with AWS, Azure, or GCP including deploying ML models, managing compute resources, understanding pricing and cost optimization, and architecting scalable solutions on cloud infrastructure.

• Technical Communication & Presentation

  Exceptional ability to communicate complex AI/ML concepts to diverse audiences including C-suite executives, technical teams, and business stakeholders. Skilled at whiteboarding sessions, technical demos, formal presentations, and creating compelling business value narratives.

• Solution Architecture & Design

  Ability to translate customer requirements into scalable technical architecture, considering trade-offs between performance, cost, maintainability, and business objectives. Experience designing end-to-end solutions leveraging modern data platforms and AI services.

Preferred

• LLamaIndex & Advanced RAG Patterns

  Nice to have

  Experience with LLamaIndex or similar frameworks for building sophisticated retrieval-augmented generation systems, including multi-modal indexing, hybrid retrieval strategies, and advanced prompt routing patterns.

• Apache Airflow Orchestration

  Nice to have

  Hands-on experience designing and maintaining Apache Airflow DAGs for complex ML workflows, data pipelines, and production scheduling at scale. Understanding of task dependencies, failure handling, and monitoring.

• dbt Data Transformation

  Nice to have

  Experience using dbt for data modeling and transformation, particularly in contexts where ML pipelines consume dbt-processed data. Understanding of how modern data tools integrate with ML workflows.

• PyTorch Deep Learning

  Nice to have

  Practical experience with PyTorch including model architecture design, training optimization, transfer learning, and fine-tuning strategies for deep learning applications including transformer models.

• Vector Databases & Semantic Search

  Nice to have

  Experience implementing vector similarity search, embedding models, and vector database platforms like Pinecone, Weaviate, or Milvus in production applications, particularly for RAG and semantic search use cases.

• A/B Testing & Experimentation

  Nice to have

  Expertise in designing and executing rigorous A/B tests and experimentation frameworks for ML model evaluation, feature validation, and business impact measurement in production systems.

• Customer-Facing Technical Leadership

  Nice to have

  Previous experience in sales engineering, solution architecture, or customer advisory roles where technical expertise was leveraged to influence customer decisions and drive adoption of sophisticated technology solutions.

• Industry Knowledge & Enterprise Compliance

  Nice to have

  Familiarity with enterprise governance, data security, regulatory compliance (HIPAA, GDPR, SOC 2), and responsible AI practices. Understanding of how these considerations impact ML architecture decisions in regulated industries.