> Markdown version of [/jobs/ext/213400-telecommute-sr-generative-ai-engineer-aws-bedrock-databricks-ai-agents](https://www.wearedevelopers.com/jobs/ext/213400-telecommute-sr-generative-ai-engineer-aws-bedrock-databricks-ai-agents). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

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# TELECOMMUTE Sr Generative AI Engineer AWS Bedrock, Databricks & AI Agents

- **Company:** Cube hub
- **Location:** San Mateo, United States (Remote available)
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Information Engineering, Python (Programming Language), Machine Learning, Standard Sql, Microsoft Power Automate, Large Language Models, Prompt Engineering, AI Platforms, Databricks
- **Published:** May 23, 2026
- **Apply:** https://www.dice.com/job-detail/a2d7820d-7c3e-4d9c-ad55-b08034a43994

## About the Role

7+ years of experience in AI engineering, data engineering, AI enablement, or applied technology roles.
Hands-on experience working with AWS (including AWS Bedrock or similar managed AI services).
Working experience with Databricks (Genie spaces, Playground, etc.)
Strong Python and SQL skills; comfortable working in notebooks and lightweight scripts.
Experience building quick prototypes and explaining technical concepts clearly to non-technical stakeholders.
Ability to work independently and move quickly in a remote, fast paced environment.
Good to Have
Familiarity with core AI/ML concepts (e.g., LLMs, embeddings, prompt engineering).
Exposure to agent-based AI patterns or evaluation frameworks.
Basic understanding of orchestration or automation tools.
Prior experience supporting early-stage AI pilots or innovation programs.
Success Metrics
Delivery of multiple working PoCs or pilots within the first 4 8 weeks.
Clear stakeholder signal on which ideas are viable and worth further investment.
Demonstrated acceleration of workflows, insights, or decision-making through AI experimentation.
Well-documented outcomes and recommendations to support next-phase scaling.

## Description

We are seeking an AI Consultant (hands-on) to support rapid experimentation, proofs of concept (PoCs), and pilot AI solutions that accelerate priority business use cases.
This role is focused on building quickly, testing ideas, and demonstrating value, leveraging existing internal platforms such as AWS, Databricks, Copilot studio & Power Automate rather than heavy production engineering.
The ideal candidate is hands on, pragmatic, and comfortable working in early stage, exploratory AI efforts where speed and learning matter most.
Key Responsibilities
Rapid PoCs & Pilots:
Build and iterate on quick-turn AI PoCs, pilots, and demos to validate ideas, workflows, and agent-based experiences. Emphasis is on speed, usability, and learning-not production hardening.
Agentic Experimentation: Familiar with agentic harness to configure and test AI agents in a controlled, repeatable way.
This includes:
Defining agent roles, prompts, tools, light weight orchestration and simple memory/state handling
Running structured experiments to test agent behaviors across scenarios
Iterating on configurations to improve usefulness, reliability, and clarity
Comparing different agent patterns (e.g., single vs. multi-step flows) and capturing learnings
AWS Usage: Use AWS Bedrock & Agentcore to build AI agents with foundation models, agents, and knowledge integrations to support use cases such as summarization, insight generation, content drafting, and workflow assistance.
Databricks Based Prototyping: Leverage Databricks for to build AI agents, lightweight data exploration, preparation, and integration into AI experiments-using notebooks and existing datasets to move fast.
Low Code / Config Driven Workflows: Favor low code or configuration-based approaches (prompt templates, reusable configs, simple orchestration patterns) to accelerate development and iteration.
Lightweight Orchestration: Connect AI components across tools (e.g., Bedrock Databricks APIs, multiple agents within same environment) using simple orchestration patterns sufficient for pilots and demonstrations.
Stakeholder Collaboration: Partner closely with product, analytics, and business teams to shape ideas, demo solutions, gather feedback, and refine concepts.
Documentation & Readouts: Clearly document PoCs, agent behaviors, findings, and recommendations so successful pilots can be evaluated for future scale-up.


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