RCI-GILD-17083 Generative AI Solutions Architect - Rapid Prototyping & AI Agents (Biotech/ FDA) - 100% REMOTE
JOIN A multi-billion-dollar life sciences pioneer known for developing breakthrough, life-saving therapies." Description: 100% REMOTE Title: AI Consultant (hands-on) The ideal candidate is hands‑on, pragmatic, and comfortable working in early‑stage, exploratory AI efforts where speed and learning matter most. 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.
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.
Required Qualifications
- 4–7+ 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 (Not Required)
- 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, i
nsights, or decision-making through AI experimentation.
- Well-documented
outcomes and recommendations to support next-phase scaling.