Nelly
Junior AI Engineer(m/w/x)
Vollzeitmit HomeofficeJunior
Berlin
Neuer Job?Nejo!
Dein persönlicher KI-Karriere-Agent
APApheris
.Agentic AI Engineer(m/w/x)
Berlin
Vollzeitmit HomeofficeBerufserfahren
AI/ML
Nejo KI-Zusammenfassung
Building AI-first data foundations for drug discovery, unifying diverse data sources for LLM access. Hands-on LLM and agent framework experience required. Early-stage virtual share options, wellbeing budget.
Anforderungen
- 2–4 years applied AI, data systems, or internal agentic tools experience
- Hands-on LLMs and retrieval-augmented systems experience
- Hands-on agent frameworks and orchestration experience
- Hands-on workflow automation across multiple systems experience
- Hands-on secure execution environments setup experience
- Solid data engineering capabilities
- Designing and maintaining data pipelines (batch/real-time)
- Building and managing structured data layers
- Integrating and normalizing data across heterogeneous sources
- Ensuring data quality, observability, and reliability
- Exceptional execution bias and entrepreneurial drive
- Experience building agentic workflows in real-world environments
- Experience integrating various data sources
- Familiarity with Claude Code, Pi (OpenClaw), or similar agent systems
- Experience integrating across communication tools, documentation systems, internal platforms
- Strong engineering and product judgment
- High bar for quality, speed, and ownership
- Flexibility to jump across topics and work with various internal teams
- Fluent English
- German optional
- Background in fast-moving startup environments
- Exposure to scientific, technical, or data-intensive domains
Aufgaben
- Build AI-first internal data foundation
- Create unified data layer across meeting transcripts, email, Slack, CRM, Confluence, product documentation, and external signals
- Design pragmatic data pipelines, schemas, and retrieval systems for LLM access
- Ensure information is structured, queryable, and reliable
- Build agentic workflows and internal AI systems
- Design and deploy agentic workflows and LLM interfaces for daily use
- Deliver pre-meeting briefings with account context and recommended actions
- Generate automated debriefs and follow-ups
- Extract customer feedback into structured product insights
- Provide cross-functional visibility into discussions and decisions
- Translate customer signals into product inputs
- Generate competitive intelligence and internal knowledge synthesis
- Create high-quality drafts for internal and external communication
- Produce marketing copy and decision dashboards for senior leadership
- Iterate based on real usage and feedback
- Drive adoption and workflow transformation
- Identify high-value workflows across commercial, product, and leadership teams
- Replace manual, fragmented processes with AI-native workflows
- Shape how teams use AI in day-to-day work
- Focus on systems that are actually used
- Turn prototypes into production-ready systems
- Move fast from prototype to reliable internal tooling
- Establish standards for data quality and consistency
- Set access control and permissions
- Implement monitoring and maintenance
- Balance speed with robustness for sustained usage
- Build secure, reliable, and non-destructive agent systems
- Enforce process isolation and strict permissioning
- Ensure predictable, auditable behavior
- Implement fail-safes, rollback mechanisms, and continuous testing
- Contribute to company-wide AI-first transformation
- Act as a key driver in making Apheris AI-native
- Bring in best practices from agentic AI, LLM tooling, and workflow automation
- Contribute to adjacent technical systems where relevant
Berufserfahrung
- 2 - 4 Jahre
Ausbildung
- Bachelor-AbschlussODER
- Master-Abschluss
Sprachen
- Englisch – fließend
- Deutsch – Grundkenntnisse
Tools & Technologien
- LLMs
- retrieval-augmented systems
- Agent frameworks
- workflow automation
- Claude Code
- Pi (OpenClaw)
- CRM
- Slack
- docs
- product systems
- data pipelines
- data warehouses
- vector databases
Benefits
Attraktive Vergütung
- Industry-competitive compensation
- Early-stage virtual share options
Sonstige Zulagen
- Wellbeing budget
- Work-from-home budget
- Co-working stipend
Mentale Gesundheitsförderung
- Mental health support
Weiterbildungsangebote
- Learning budget
Mehr Urlaubstage
- Generous holiday allowance
Team Events & Ausflüge
- Office days in Berlin HQ or European location (3x per year)
Die Originalanzeige dieses Stellenangebotes in der aktuellsten Version findest du hier. Nejo hat diesen Job automatisch von der Website des Unternehmens Apheris erfasst und die Informationen auf Nejo mit Hilfe von KI für dich aufbereitet. Trotz sorgfältiger Analyse können einzelne Informationen unvollständig oder ungenau sein. Bitte prüfe immer alle Angaben in der Originalanzeige! Inhalte und Urheberrechte der Originalanzeige liegen beim ausschreibenden Unternehmen.
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APApheris
.Agentic AI Engineer(m/w/x)
Berlin
Vollzeitmit HomeofficeBerufserfahren
AI/ML
Nejo KI-Zusammenfassung
Building AI-first data foundations for drug discovery, unifying diverse data sources for LLM access. Hands-on LLM and agent framework experience required. Early-stage virtual share options, wellbeing budget.
Anforderungen
- 2–4 years applied AI, data systems, or internal agentic tools experience
- Hands-on LLMs and retrieval-augmented systems experience
- Hands-on agent frameworks and orchestration experience
- Hands-on workflow automation across multiple systems experience
- Hands-on secure execution environments setup experience
- Solid data engineering capabilities
- Designing and maintaining data pipelines (batch/real-time)
- Building and managing structured data layers
- Integrating and normalizing data across heterogeneous sources
- Ensuring data quality, observability, and reliability
- Exceptional execution bias and entrepreneurial drive
- Experience building agentic workflows in real-world environments
- Experience integrating various data sources
- Familiarity with Claude Code, Pi (OpenClaw), or similar agent systems
- Experience integrating across communication tools, documentation systems, internal platforms
- Strong engineering and product judgment
- High bar for quality, speed, and ownership
- Flexibility to jump across topics and work with various internal teams
- Fluent English
- German optional
- Background in fast-moving startup environments
- Exposure to scientific, technical, or data-intensive domains
Aufgaben
- Build AI-first internal data foundation
- Create unified data layer across meeting transcripts, email, Slack, CRM, Confluence, product documentation, and external signals
- Design pragmatic data pipelines, schemas, and retrieval systems for LLM access
- Ensure information is structured, queryable, and reliable
- Build agentic workflows and internal AI systems
- Design and deploy agentic workflows and LLM interfaces for daily use
- Deliver pre-meeting briefings with account context and recommended actions
- Generate automated debriefs and follow-ups
- Extract customer feedback into structured product insights
- Provide cross-functional visibility into discussions and decisions
- Translate customer signals into product inputs
- Generate competitive intelligence and internal knowledge synthesis
- Create high-quality drafts for internal and external communication
- Produce marketing copy and decision dashboards for senior leadership
- Iterate based on real usage and feedback
- Drive adoption and workflow transformation
- Identify high-value workflows across commercial, product, and leadership teams
- Replace manual, fragmented processes with AI-native workflows
- Shape how teams use AI in day-to-day work
- Focus on systems that are actually used
- Turn prototypes into production-ready systems
- Move fast from prototype to reliable internal tooling
- Establish standards for data quality and consistency
- Set access control and permissions
- Implement monitoring and maintenance
- Balance speed with robustness for sustained usage
- Build secure, reliable, and non-destructive agent systems
- Enforce process isolation and strict permissioning
- Ensure predictable, auditable behavior
- Implement fail-safes, rollback mechanisms, and continuous testing
- Contribute to company-wide AI-first transformation
- Act as a key driver in making Apheris AI-native
- Bring in best practices from agentic AI, LLM tooling, and workflow automation
- Contribute to adjacent technical systems where relevant
Berufserfahrung
- 2 - 4 Jahre
Ausbildung
- Bachelor-AbschlussODER
- Master-Abschluss
Sprachen
- Englisch – fließend
- Deutsch – Grundkenntnisse
Tools & Technologien
- LLMs
- retrieval-augmented systems
- Agent frameworks
- workflow automation
- Claude Code
- Pi (OpenClaw)
- CRM
- Slack
- docs
- product systems
- data pipelines
- data warehouses
- vector databases
Benefits
Attraktive Vergütung
- Industry-competitive compensation
- Early-stage virtual share options
Sonstige Zulagen
- Wellbeing budget
- Work-from-home budget
- Co-working stipend
Mentale Gesundheitsförderung
- Mental health support
Weiterbildungsangebote
- Learning budget
Mehr Urlaubstage
- Generous holiday allowance
Team Events & Ausflüge
- Office days in Berlin HQ or European location (3x per year)
Die Originalanzeige dieses Stellenangebotes in der aktuellsten Version findest du hier. Nejo hat diesen Job automatisch von der Website des Unternehmens Apheris erfasst und die Informationen auf Nejo mit Hilfe von KI für dich aufbereitet. Trotz sorgfältiger Analyse können einzelne Informationen unvollständig oder ungenau sein. Bitte prüfe immer alle Angaben in der Originalanzeige! Inhalte und Urheberrechte der Originalanzeige liegen beim ausschreibenden Unternehmen.
Über das Unternehmen
Apheris
Branche
Pharmaceuticals
Beschreibung
Apheris builds AI applications for pharmaceutical R&D, enabling secure collaboration on large AI models to accelerate drug discovery.
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- Nelly
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