- Company Name
- Quotient Sciences
- Job Title
- AI Engineer
- Job Description
-
**Job Title:** AI Engineer
**Role Summary:**
Own the end‑to‑end AI lifecycle—data ingestion, model development, deployment, and monitoring—to deliver scalable AI solutions that align with strategic objectives. Serve as a technical leader, ensuring responsible AI practices, governance, and compliance while collaborating with product, data engineering, and business stakeholders.
**Expectations:**
- Deliver production‑grade AI systems that generate measurable business value.
- Uphold responsible AI principles, governance, and compliance (including GxP where applicable).
- Communicate complex technical concepts clearly to both technical and non‑technical audiences.
- Mentor teammates and contribute to a collaborative, high‑performance culture.
**Key Responsibilities:**
- Design, develop, and deploy machine learning and deep learning models, balancing performance, interpretability, and operational fit.
- Build and maintain scalable ML pipelines and infrastructure (classical ML and deep learning).
- Deploy models to production using containerisation, CI/CD, and MLOps tools; manage ongoing configuration and administration.
- Develop LLM‑based tools (prompt engineering, retrieval, embedding pipelines) for knowledge retrieval and workflow assistance.
- Build APIs, microservices, or workflow components to integrate AI solutions into existing systems.
- Implement monitoring for model drift, performance, latency, and failures; maintain logging and observability.
- Embed responsible AI practices and governance into all solutions; follow validation and compliance standards where required.
- Translate business requirements into robust technical solutions in collaboration with cross‑functional teams.
- Produce clear documentation for models, pipelines, deployment steps, and operational expectations.
- Stay current with AI/ML advancements and help shape common frameworks and best practices across the organization.
**Required Skills:**
- Proven experience in AI engineering, machine learning, or data science roles.
- Track record of building, deploying, and maintaining production‑grade AI models and pipelines.
- Strong proficiency in Python, R, and ML frameworks (TensorFlow, PyTorch, Scikit‑learn).
- Experience with cloud platforms and ML infrastructure (AWS SageMaker, MLflow, etc.).
- Practical understanding of monitoring, logging, CI/CD, and containerisation.
- Experience with LLMs, vector search, or retrieval‑augmented systems.
- Familiarity with responsible AI practices, data governance, and compliance frameworks.
- Knowledge of Agile principles (Kanban, Scrum) and roadmap delivery tools (Jira).
- Excellent communication skills; ability to explain complex concepts to non‑technical stakeholders.
- Prior exposure to life sciences, biotech, or manufacturing is desirable; understanding of CDMO processes and GxP/regulatory environments is a plus.
**Required Education & Certifications:**
- Bachelor’s degree (or higher) in Computer Science, Engineering, Data Science, or a related technical field, or equivalent industry experience.
- Certifications in machine learning, cloud platforms (e.g., AWS, GCP, Azure), or data science are acceptable and may be preferred.
Nottingham, United kingdom
On site
Senior
17-12-2025