At a Glance
- Tasks: Architect and deploy innovative AI workflows to solve real business challenges.
- Company: Fast-growing AI start-up redefining biology with cutting-edge technology.
- Benefits: Competitive salary, equity package, flexible remote work, and growth opportunities.
- Why this job: Shape the future of AI in biomedicine and make a real impact.
- Qualifications: STEM degree and 2-5 years in applied ML or MLOps required.
- Other info: Collaborative culture focused on inclusivity and professional development.
The predicted salary is between 48000 - 84000 ÂŁ per year.
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 startâup headquartered in Paris, incorporated in October 2023. Backed by leading international venture capitalists, our worldâclass 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 crossâfunctional 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 crossâfunctional teams.
What youâll be doing
- Build & Deploy Agentic AI Systems
- Design and implement productionâgrade agentic systems automating/augmenting internal operations.
- Build multiâstep agents using LLMs, RAG pipelines, orchestration frameworks, and custom toolâuse 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, fineâtuned variants) when required for functional workflows.
- Optimize prompts, retrieval strategies, guardrails, and agent policies using principles from fineâtuning or RLâstyle optimization.
- Ensure stability, correctness, and reliability for businessâcritical automations.
- Technical Product Building â 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 â design â build â deploy â iterate.
- Communicate decisions and constraints clearly to both technical and nonâtechnical stakeholders.
- Build CI/CD pipelines for prompts, models, tools, and agent behaviour.
- Deploy agents and services using Docker, AWS, serverâless workflows, or lightweight microâservices.
- 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, wellâdocumented codebases and system diagrams.
- CrossâFunctional Collaboration & Rollout Support
- Continuously track rapidlyâchanging technological trends and SOTA to make the best build vs. buy recommendations to the function challenges.
- Train endâusers 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 youâll 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 highâgrowth, technologyâdriven environment. You thrive in an ambiguous environment and naturally combine:
- Business intuition + technical depth
- Curiosity + ownership + clarity of communication
Skills & Experience
- Education: STEM degree (Computer Science, AI, Data Science, Applied Mathematics, or Engineering); or Dualâdegree blending business and ML/data science (e.g., engineering school + management/innovation program)
- 2â5 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 endâtoâend, from scoping and modeling to deployment and iteration.
- Familiarity with LLMs, prompting, RAG, orchestration, fineâtuning.
How to stand out:
- Experience building agentic or multiâstep 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, multiâstep reasoning).
- Experience in clientâfacing AI/ML consulting engagements.
The Candidate Journey
To be considered, please submit your CV in English. Accepted file types: pdf, doc, docx, txt, rtf
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 functionâFinance, 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 missionâdriven 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.
AI Technical Operations Manager in London employer: Bioptimus
Contact Detail:
Bioptimus Recruiting Team
StudySmarter Expert Advice đ¤Ť
We think this is how you could land AI Technical Operations Manager in London
â¨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
â¨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those related to AI and ML. This gives you a chance to demonstrate your technical prowess and problem-solving abilities to potential employers.
â¨Tip Number 3
Prepare for interviews by practising common questions and scenarios specific to AI Technical Operations. Think about how you would approach real-world problems and be ready to discuss your thought process and solutions.
â¨Tip Number 4
Donât forget to apply through our website! Itâs the best way to ensure your application gets seen by the right people. Plus, it shows youâre genuinely interested in joining our mission at Bioptimus.
We think you need these skills to ace AI Technical Operations Manager in London
Some tips for your application đŤĄ
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the AI Technical Operations Manager role. Highlight your ML engineering, MLOps experience, and any relevant projects that showcase your ability to solve real business problems.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about AI and how your background makes you a great fit for Bioptimus. Be specific about how you can contribute to our mission of redefining AI in biomedicine.
Showcase Your Technical Skills: Donât just list your technical skills; demonstrate them! Include examples of projects where you've built or deployed ML systems, especially those involving LLMs or automation. This will help us see your hands-on experience.
Apply Through Our Website: We encourage you to apply directly through our website. Itâs the best way for us to receive your application and ensures youâre considered for this exciting opportunity at Bioptimus!
How to prepare for a job interview at Bioptimus
â¨Know Your Tech Inside Out
Make sure youâre well-versed in the technologies mentioned in the job description, like LLMs, MLOps, and Docker. Brush up on your Python and SQL skills, and be ready to discuss how you've used these tools in past projects.
â¨Understand the Business Side
Since this role involves translating business needs into technical solutions, take time to research Bioptimus and its operations. Think about how AI can solve real-world problems in biomedicine and be prepared to share your insights during the interview.
â¨Prepare for Scenario Questions
Expect questions that ask you to solve hypothetical problems or design systems on the spot. Practice articulating your thought process clearly, as communication is key. Use examples from your experience to demonstrate your approach to problem-solving.
â¨Show Your Collaborative Spirit
This role requires working closely with various teams. Be ready to discuss how youâve successfully collaborated with non-technical stakeholders in the past. Highlight any experiences where youâve trained users or gathered feedback to improve systems.