FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates strong full-stack and AI/ML engineering capabilities, with proficiency in Python and experience in production deployments. Skilled in automating scientific workflows and collaborating with cross-functional teams to deliver effective AI solutions.
Highest-signal resume keywords
Full-Stack EngineeringAI/ML EngineeringPython ProficiencyAgent Frameworks ExperienceProduction Deployments
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI Coding AgentsMulti-Agent OrchestrationTool UseModel Context Protocol (MCP)Data Pipeline AutomationAnalysis Code GenerationHuman-in-the-Loop ReviewPrototypingSoftware DevelopmentProduction Deployments
Soft Skills
CoachingCollaborationCommunicationProblem FramingJudgment
Tools & Technologies
Claude CodeGitHub CopilotDatabricks AI AssistantAgentic AutomationR&D Technology
Certifications & Qualifications
Bachelor’s Degree in Computer ScienceBachelor’s Degree in Software Engineering
Industry Keywords
Life Sciences R&DProcess DevelopmentRegulated IndustryGxPGDPRSOX
Tech Stack
Tools & technologiesJavaScriptPythonTypeScriptGo
About the role
Key responsibilities & impact- Own high-priority business-unit use cases from problem framing through working PoCs, PoVs, and MVPs at a days-to-weeks cadence
- Assess whether use cases should progress and form defensible kill/continue recommendations
- Ship first production versions where the path is clear before handoff to the Senior Solutions Engineer
- Operate AI agent workflows for planning, coding, testing, review, and documentation
- Use AI coding assistants as the default development mode, applying judgement, course correction, and validation
- Embed with business-unit teams, attend standups, understand domain needs, and prototype using real data and workflows
- Package proven use cases for the Senior Solutions Engineer with working code, architecture rationale, open risks, and scaling concerns
- Initiate Code Orange, Responsible AI, and Accountability Report conversations during prototyping
- Contribute reusable agents, prompt templates, and reference architectures to the organisation’s pattern library
- Present kill/continue recommendations to business-unit and Enterprise AI leadership
- Coach business-unit engineers on AI-augmented ways of working
- Build agentic automation for CMC R&D process development, including data capture, analysis, and experimental planning
- Partner with process-science data analytics and R&D Technology teams
Requirements
What you’ll need- Strong full-stack or AI/ML engineering experience, including production deployments
- Strong proficiency in Python and at least one of TypeScript/JavaScript, Go, or similar
- Ability to take ideas from sketch to working software rapidly with R&D scientists as end users
- Hands-on experience with agent frameworks, multi-agent orchestration, tool use, Model Context Protocol (MCP), agent evaluation, and tracing
- Experience automating scientific or technical workflows with agents, including lab and process-system data pipelines, analysis code generation, and human-in-the-loop review
- Daily use of AI coding agents such as Claude Code, GitHub Copilot, or Databricks AI Assistant as the primary development mode
- Bachelor’s degree in Computer Science, Software Engineering, or equivalent practical experience
- Experience must include production deployments
- Preferred: Forward Deployed Engineer, Customer Engineer, or Solutions Architect experience
- Preferred: Life sciences R&D or process development experience working directly with scientists
- Preferred: Regulated-industry experience and familiarity with GxP, GDPR, and SOX
- Preferred: Experience partnering with data analytics, data science, or process science teams
- Preferred: Track record of contributing reusable engineering patterns to a broader team
Benefits
Comp & perks- Hybrid working arrangement with a minimum of 2/3 days per week onsite
- Learning and development opportunities to grow technical and leadership skills
- Supportive team culture focused on clear communication and practical problem solving
- Agile working culture and flexibility opportunities
- Adjustments and support during the recruitment process
- Equal consideration regardless of protected characteristics