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)
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 (supervised learning, unsupervised learning, or light generative AI applications).
Ability to evaluate and debug ML systems using appropriate metrics (AUC, precision/recall, F1, clustering validity, drift diagnostics).
Experience delivering ML or automation projects end-to-end, from scoping and modeling to deployment and iteration.
Familiarity with LLMs, prompting, RAG, orchestration, fine-tuning.

How to stand out:

Experience building agentic or multi-step LLM systems (LangChain, CrewAI, LlamaIndex).
Working knowledge of vector databases (e.g., Weaviate, Pinecone).
Experience with workflow automation platforms (e.g., n8n, Make, Zapier).
Exposure to RL concepts (reward shaping, constraints, multi-step reasoning).
Experience in client-facing AI/ML consulting engagements.

The Candidate Journey

To be considered, please submit your CV in English.

We believe in a transparent and collaborative interview process. Here is what you can expect after submitting your application:

Screening: Once you have applied, the hiring team will review your application.
Technical Assessment: Given the technical nature of the role, you will be invited to complete a timed (1.5 hours) technical assessment on a dedicated platform. This stage consists of a short set of Python exercises designed to assess your coding proficiency. Importantly, this preliminary screening stage will be followed by live coding interviews (see below), so that the candidate should not rely on external AI-based tools here.
Interviews: Following a successful screening, you will be invited to the core stages of our evaluation process:
- Hiring Manager (30 min): A call with the Hiring Manager to discuss your background, motivation, and expertise. This will include a deep dive into your technical skills, including your experience with coding and cloud environments.
- Technical Challenge (Take-home): You will be invited to complete a practical exercise focused on building an LLM-driven agentic system. This is designed to assess your coding standards, system thinking, and ability to evaluate complex AI outputs. You will present your solution to the team. This 1-hour session will cover your live demo, system design choices, deployment roadmap, and thoughts on user adoption. Expect a deep-dive discussion on the technical and strategic decisions you made.
- Culture & Team Fit (Series of 30-min chats): You will meet in-person with cross-functional members of our team. This is a chance for us to assess your "community builder" mindset and for you to gauge the culture you will be supporting.
- Leadership Interview (30 min): A final conversation to discuss your vision for the role, assess executive maturity, and ensure alignment on expectations.
Offer: Following the completion of all interviews, our hiring team will make a final decision and will be in touch to share the outcome. If the team would like to move forward, we will contact you to discuss the details of our proposed offer. Please note that an offer is contingent upon the successful completion of a reference check.
Onboarding: We are happy to have you joining the team! Once you have accepted and signed your offer, we will be in touch to begin the process of onboarding you.

Why this is a unique opportunity

You will:

Build the agentic systems that run the company.
Translate complex business workflows into elegant AI/ML systems.
Be a multiplier for every function—Finance, HR, Legal, Marketing, Product, R&D.
Shape automation strategy and architecture within a frontier AI lab.
Gain rare experience in applied LLM orchestration + MLOps + product building.

And benefit from:

A collaborative and mission-driven work environment.
Competitive salary and equity package.
Flexible work arrangements, including remote options.
Opportunities for professional growth and leadership development.
Shape the future of biology and AI by contributing to groundbreaking work.

We believe that the unique contributions of all Bioptimists create our success. To ensure that our culture continues to incorporate everyone’s perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, or disability status. Decisions related to hiring are made fairly, and we provide equal employment opportunities to all qualified candidates. We take responsibility for always striving to create an inclusive environment that makes every employee and candidate feel welcome.

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