- Company Name
- Epoca
- Job Title
- AI Engineer - ML/LLMOps
- Job Description
-
Job Title: AI Engineer – ML/LLM Ops
Role Summary:
Design, integrate, and productionise AI/ML models (including vision, predictive, and LLM) for a healthcare tele‑monitoring platform. Drive end‑to‑end MLOps/LLMOps processes, ensuring fast deployment, robust monitoring, and compliance with data‑privacy regulations.
Expectations:
• Execute end‑to‑end model lifecycle from research to production, delivering business value quickly.
• Architect scalable, secure AI pipelines in cloud environments.
• Collaborate cross‑functionally with data science, product, and clinical teams.
Key Responsibilities:
- Develop and operationalise AI/ML solutions (LLM, vision, predictive) using Python, Hugging Face, and cloud AI services.
- Build, test, and deploy rapid prototypes into production, applying MLOps best practices (CI/CD, model registry, versioning).
- Implement monitoring, A/B testing, and performance dashboards to track model drift and usage.
- Conduct feature engineering on medical documents, speech-to-text transcripts, and dynamic coaching content.
- Ensure data security and regulatory compliance (GDPR, HDS) through secure architecture and responsible data handling.
- Lead pilot projects: POCs, prototypes, integration, and full industrialisation, delivering measurable outcomes.
- Stay current on AI tools (LangChain, DSPy, LlamaIndex, etc.) and evaluate new solutions for adoption.
- Coordinate with Lead Data, Data Science, Clinical (Care), Product, and Executive teams.
Required Skills:
- Strong Python programming and familiarity with AI frameworks (PyTorch/TensorFlow).
- Proven experience deploying pretrained models using Hugging Face Transformers, LLMs, and vision models.
- Hands‑on experience with MLOps tools (MLflow, Kubeflow, Airflow) and cloud AI services (Azure AI, GCP AI/Vertex, AWS SageMaker).
- Knowledge of orchestration, evaluation, and optimisation libraries (LangChain, DSPy, LlamaIndex).
- Expertise in CI/CD pipelines, model monitoring, and performance optimisation.
- Understanding of data‑privacy laws (GDPR, HDS) and secure cloud architecture.
- Strong problem‑solving, communication, and project‑management skills.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Biomedical Engineering, or related field.
- Certifications in Cloud AI services (e.g., Azure AI Engineer Associate, GCP Professional Machine Learning Engineer) are a plus.