AI Architect

Bitwise; United States; Remote; USD 180,000 - 230,000; Full time

Benefits offered by this job

Remote work

Flexible schedule

Job summary

Bitwise is seeking an AI Architect to lead the design and delivery of AI-driven data modeling, documentation, and automation solutions. This remote role focuses on building agentic, scalable workflows with Databricks-based pipelines and governance-compliant practices.

You will guide a small delivery team and serve as the primary technical liaison with client data platforms and engineers, shaping strategy and execution across data quality, ETL/ELT patterns, and reusable GenAI components.

Qualifications

• 10 years in data engineering or applied AI/ML, incl. 2 years leading delivery teams.
• Experience with Databricks Platform and multi-step AI workflows.
• Experience building LLM-powered apps (prompt, context, RAG).
• Integrating LLMs into data pipelines in governed environments.
• Knowledge of guardrails and responsible-AI practices.

Responsibilities

• Own the technical roadmap for AI-driven data modeling, docs, data quality rules, and transformation code generation.
• Design agentic workflow architecture using LangGraph, CrewAI, or AutoGen.
• Ensure AI workflows comply with client AI governance/approval framework.
• Lead a delivery team through discovery, prioritization, and iterative delivery of automation.
• Act as primary technical contact with client platform and data engineering stakeholders.

Skills

Data engineering

Applied AI/ML

AI governance

Databricks

LLM integration

Prompt engineering

RAG pipelines

Multi-agent patterns

Team leadership

Python

PySpark

Declarative pipelines

Tools

Databricks

GitHub Copilot

Claude Code

Python

PySpark

Job description

Job Description
AI Architect - AI & Automation
Work Location: Remote

Key Responsibilities

• Own the technical roadmap for AI-driven data modeling, documentation generation, data quality rule generation, and transformation code generation.
• Design agentic workflow architecture using frameworks such as LangGraph, CrewAI, or AutoGen.
• Ensure all AI workflows operate within the client's AI governance and approval framework.
• Lead a small delivery team (AI Tech Lead, AI Developer) through discovery, prioritization, and iterative delivery of automation solutions.
• Act as the primary technical point of contact with client platform and data engineering stakeholders.
• Required Qualifications 10 years in data engineering or applied AI/ML, including 2 years leading applied-AI delivery teams.
• Proficiency on Databricks Platform Experience designing agentic or multi-step AI workflows (LangGraph, CrewAI, AutoGen, or equivalent).
• Experience building LLM-powered applications, including prompt engineering, context engineering, and Retrieval-Augmented Generation (RAG).
• Experience integrating large language models into data engineering pipelines within a governed enterprise environment.
• Experience with LLM evaluation and human-in-the-loop validation workflows to ensure accuracy and reliability of AI-generated outputs; working knowledge of guardrails and responsible-AI practices.
• Experience building reusable, modular GenAI components (e.g., plugins) using structured multi-agent patterns to enable repeatable automation.
• Experience deploying, versioning, monitoring, and evaluating LLM solutions in production, including prompt/version management and feedback-loop mechanisms.
• Strong understanding of ETL/ELT patterns, schema analysis, source-to-target mapping, data quality, data profiling and data modeling.
• Experience in designing reusable framework integration patterns with Databricks Python, PySpark/Declarative pipelines
• Understanding of Data Engineering frameworks such as Data Quality, Data Modelling, Metadata Enrichment, Sensitive data handling, STTM, Modular and testable Python or PySpark/Declarative pipeline code generation.
• Experience in designing performance and cost optimized data engineering frameworks Client-facing consulting or delivery experience.
• Experience leveraging Agentic coding platforms (for example, GitHub Copilot or Claude Code) effectively.

Preferred Qualifications

• Experience with Databricks AI/ML tooling.
• Background in data quality automation or catalog/metadata generation.