More jobs:
AI Architect
Job in
Washington, District of Columbia, 20022, USA
Listed on 2026-10-10
Listing for:
Bitwise
Full Time
position Listed on 2026-10-10
Job specializations:
-
Software Development
AI Engineer (Applied/Software), Data Engineering, AI QA / Validation Engineer, Machine Learning/ ML Engineer
Job Description & How to Apply Below
Job Description
AI Architect - AI & Automation
Work Location:
Remote
- 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 Lang Graph, CrewAI, or Auto Gen.
- 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 (Lang Graph, CrewAI, Auto Gen, 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, Git Hub Copilot or Claude Code) effectively.
- Experience with Databricks AI/ML tooling.
- Background in data quality automation or catalog/metadata generation.
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