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AI Technical Operations Manager(m/w/x)
Building agentic/LLM solutions for a universal AI foundation model at a biomedicine AI developer. Hybrid applied ML engineer/MLOps builder mindset with 2-5 years experience required. Equity package.
Anforderungen
- ML intuition, engineering capability, product mindset, and strong communication skills
- Hybrid applied ML engineer, MLOps builder, product thinker
- Thriving in ambiguous environments
- STEM degree or dual-degree blending business and ML/data science
- 2–5 years in applied ML, AI/data consulting, ML engineering, or MLOps
- Proficiency in Python and SQL
- Experience with Docker, AWS, CI/CD, and deploying ML systems
- Experience building and deploying ML models
- Ability to evaluate and debug ML systems using appropriate metrics
- Experience delivering ML or automation projects end-to-end
- Familiarity with LLMs, prompting, RAG, orchestration, fine-tuning
- Experience building agentic or multi-step LLM systems
- Working knowledge of vector databases
- Experience with workflow automation platforms
- Exposure to RL concepts
- Experience in client-facing AI/ML consulting engagements
Aufgaben
- Understand business workflows and requirements
- Design systems to address operational challenges
- Build agentic and LLM solutions
- Deploy and monitor solutions in production
- Iterate based on performance feedback
- Design and implement production-grade agentic systems
- Build multi-step agents using LLMs and orchestration frameworks
- Integrate classical ML models into workflows
- Deploy ML models in production environments
- Create generative AI components as needed
- Optimize prompts and retrieval strategies
- Ensure stability and reliability of automations
- Partner with various departments to identify automation opportunities
- Translate business needs into technical specifications
- Evaluate ROI and feasibility of solutions
- Manage solutions through the full lifecycle
- Communicate technical decisions to stakeholders
- Build CI/CD pipelines for models and tools
- Deploy services using Docker and AWS
- Implement evaluation frameworks for system performance
- Monitor systems for drift and degradation
- Maintain well-documented codebases and diagrams
- Track technological trends for build vs. buy decisions
- Train end-users on new systems and gather feedback
- Refine workflows with business functions
- Advise leadership on automation and architecture
- Build systems that enhance company operations
- Translate complex workflows into AI/ML systems
- Shape automation strategy within an AI lab
Berufserfahrung
- 2 - 5 Jahre
Ausbildung
- Bachelor-Abschluss
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- Python
- SQL
- Docker
- AWS
- CI/CD
Benefits
Flexibles Arbeiten
- Flexible work arrangements
- Remote options
Attraktive Vergütung
- Competitive salary
- Equity package
Karriere- und Weiterentwicklung
- Opportunities for professional growth
- Leadership development
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AI Technical Operations Manager(m/w/x)
Building agentic/LLM solutions for a universal AI foundation model at a biomedicine AI developer. Hybrid applied ML engineer/MLOps builder mindset with 2-5 years experience required. Equity package.
Anforderungen
- ML intuition, engineering capability, product mindset, and strong communication skills
- Hybrid applied ML engineer, MLOps builder, product thinker
- Thriving in ambiguous environments
- STEM degree or dual-degree blending business and ML/data science
- 2–5 years in applied ML, AI/data consulting, ML engineering, or MLOps
- Proficiency in Python and SQL
- Experience with Docker, AWS, CI/CD, and deploying ML systems
- Experience building and deploying ML models
- Ability to evaluate and debug ML systems using appropriate metrics
- Experience delivering ML or automation projects end-to-end
- Familiarity with LLMs, prompting, RAG, orchestration, fine-tuning
- Experience building agentic or multi-step LLM systems
- Working knowledge of vector databases
- Experience with workflow automation platforms
- Exposure to RL concepts
- Experience in client-facing AI/ML consulting engagements
Aufgaben
- Understand business workflows and requirements
- Design systems to address operational challenges
- Build agentic and LLM solutions
- Deploy and monitor solutions in production
- Iterate based on performance feedback
- Design and implement production-grade agentic systems
- Build multi-step agents using LLMs and orchestration frameworks
- Integrate classical ML models into workflows
- Deploy ML models in production environments
- Create generative AI components as needed
- Optimize prompts and retrieval strategies
- Ensure stability and reliability of automations
- Partner with various departments to identify automation opportunities
- Translate business needs into technical specifications
- Evaluate ROI and feasibility of solutions
- Manage solutions through the full lifecycle
- Communicate technical decisions to stakeholders
- Build CI/CD pipelines for models and tools
- Deploy services using Docker and AWS
- Implement evaluation frameworks for system performance
- Monitor systems for drift and degradation
- Maintain well-documented codebases and diagrams
- Track technological trends for build vs. buy decisions
- Train end-users on new systems and gather feedback
- Refine workflows with business functions
- Advise leadership on automation and architecture
- Build systems that enhance company operations
- Translate complex workflows into AI/ML systems
- Shape automation strategy within an AI lab
Berufserfahrung
- 2 - 5 Jahre
Ausbildung
- Bachelor-Abschluss
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- Python
- SQL
- Docker
- AWS
- CI/CD
Benefits
Flexibles Arbeiten
- Flexible work arrangements
- Remote options
Attraktive Vergütung
- Competitive salary
- Equity package
Karriere- und Weiterentwicklung
- Opportunities for professional growth
- Leadership development
Über das Unternehmen
Bioptimus
Branche
Other
Beschreibung
The company is building the first universal AI foundation model for biology to fuel breakthrough discoveries and accelerate innovation in biomedicine.
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