- Company Name
- James Fisher and Sons plc
- Job Title
- Principal Machine Learning Engineer
- Job Description
-
**Job Title**
Principal Machine Learning Engineer
**Role Summary**
Lead the end‑to‑end design, development and deployment of scalable, secure machine learning solutions in the energy industry. Drive MLOps practices, cloud‑native infrastructure, and advanced modeling for time‑series, streaming and sensor data while mentoring a growing ML team and translating technical outcomes into business value.
**Expectations**
- Deliver production‑ready ML systems that meet performance, cost, and regulatory standards.
- Champion best‑practice MLOps, ensuring reproducibility, monitoring, and governance.
- Influence enterprise AI strategy and communicate complex concepts to senior stakeholders.
**Key Responsibilities**
- Architect and deploy end‑to‑end ML pipelines (data ingestion, feature engineering, training, serving).
- Implement CI/CD workflows, automated testing, monitoring, and model governance.
- Design cloud‑native ML infrastructure (AWS, Azure, or GCP) using Terraform, Kubernetes, Docker.
- Research, prototype, and productionise models for time‑series, streaming and sensor data, including deep learning, LLM fine‑tuning and RAG.
- Optimize scalability, maintainability and cost‑efficiency of ML workloads.
- Collaborate with cross‑functional teams to align solutions with business objectives, regulatory requirements and ethical standards.
- Mentor and coach junior ML engineers and data scientists, fostering technical excellence.
- Establish rigorous documentation, monitoring, and compliance standards for deployed ML systems.
- Translate ML concepts into clear business value for senior leadership and drive enterprise AI strategy.
**Required Skills**
- Proficiency in Python and core data science libraries (NumPy, Pandas, Scikit‑Learn, PyTorch, TensorFlow).
- Deep learning, time‑series modeling and large‑language‑model expertise (fine‑tuning, retrieval‑augmented generation).
- Hands‑on experience with cloud platforms (AWS, Azure, GCP) and IaC (Terraform).
- Containerization and orchestration (Docker, Kubernetes).
- MLOps lifecycle management: reproducibility, CI/CD, model governance, monitoring.
- Distributed computing and real‑time streaming (Kafka, Spark, Ray).
- Strong communication, stakeholder engagement, and mentoring capabilities.
- Experience delivering solutions in regulated or safety‑critical environments.
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science or a related field.
- Relevant cloud certifications (AWS Certified Machine Learning – Specialty, Azure AI Engineer Associate, GCP Professional Data Engineer) and optional MLOps or ML developer certifications (TensorFlow Developer, PyTorch Certification) are preferred.