AI Technical Operations Specialist

Bioptimus is building the first universal AI foundation model for biology to fuel breakthrough discoveries and accelerate innovation in biomedicine. With more than $75M in funding, Bioptimus is a fast-growing start-up headquartered in Paris, incorporated in October 2023. Backed by leading international venture capitalists, our world-class team of scientists and engineers is redefining the frontiers of AI and life sciences.

Location: Paris / London / Berlin / Remote (EU)

About the role

As AI Technical Operations Manager, you will architect, build, and deploy cross-functional agentic AI workflows. You will partner with business stakeholders to understand real operational problems, translate them into technical system designs, and implement them using LLMs, agents, RAG, orchestration, and MLOps best practices.

This role requires ML intuition, engineering capability, product mindset, and strong communication skills. You will:

• Understand business workflows and requirements
• Design the system to solve them
• Build the agentic/LLM solution
• Deploy and monitor it in production
• Iterate based on performance

You will report to the COO and collaborate closely with Engineering, Operations, and cross-functional teams.

What you’ll be doing

As our AI Technical Operations Manager, you will have directly contribute to the following strategic domains:

1. Build & Deploy Agentic AI Systems

• Design and implement production-grade agentic systems automating/augmenting internal operations.
• Build multi-step agents using LLMs, RAG pipelines, orchestration frameworks, and custom tool-use logic.
• Integrate classical ML models into workflows (supervised, unsupervised, or clustering where relevant) and deploy them in production.
• Build simple generative AI components (text models, embeddings, fine-tuned variants) when required for functional workflows.
• Optimize prompts, retrieval strategies, guardrails, and agent policies using principles from fine-tuning or RL-style optimization.
• Ensure stability, correctness, and reliability for business-critical automations.

2. Technical Product Building – Business / ML Integration

• Partner with Finance, HR, Legal, Marketing, Product, and R&D to map their workflows and identify automation opportunities.
• Translate ambiguous business needs into clear technical specifications and system architectures.
• Evaluate ROI, feasibility, risk, and adoption complexity.
• Own solutions through the full lifecycle: requirements → design → build → deploy → iterate.
• Communicate decisions and constraints clearly to both technical and non-technical stakeholders.

3. MLOps, Engineering & Infrastructure

• Build CI/CD pipelines for prompts, models, tools, and agent behavior.
• Deploy agents and services using Docker, AWS, server-less workflows, or lightweight micro-services.
• Implement evaluation frameworks for accuracy, reliability, latency, and safety.
• Build observability loops to monitor systems drift, agent degradation, failure modes; implement guardrails and alerts.
• Maintain clean, reproducible, well-documented codebases and system diagrams.

4. Cross-Functional Collaboration & Rollout Support

• Continuously track rapidly-changing technological trends and SOTA to make the best build vs. buy recommendations to the function challenges
• Train end-users on new systems and gather structured feedback.
• Work with business functions to refine workflows and embed agents sustainably.
• Be a technical advisor to leadership on automation opportunities and architecture evolution.

What you’ll bring

The successful candidate is a hybrid applied ML engineer + MLOps builder + product thinker who enjoys solving real business problems with technical systems in a high-growth, technology-driven environment.

You thrive in ambiguous environment and naturally combine:

• Business intuition + technical depth
• System design + ML evaluation + deployment discipline
• Product mindset + engineering execution
• Curiosity + ownership + clarity of communication

Skills & Experience

• Education: STEM degree (Computer Science, AI, Data Science, Applied Mathematics, or Engineering); or Dual-degree blending business and ML/data science (e.g., engineering school + management/innovation program)