Lead ML/AI Platform Engineer
J
JobgetherFinancial Technology
Based in United Kingdom, Reliable overlap with US Pacific business hoursContractLead
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Experience
- 8+ years in software or ML engineering, including at least 5 years delivering production machine learning systems
- Required Skills
- PythonSQLJavaSparkMLOpsGenerative AI
Requirements
- 8+ years in software or ML engineering, with at least 5 years delivering production machine learning systems.
- Proven experience shaping ML strategy and mentoring senior engineers.
- Hands-on experience with Amazon SageMaker, Amazon Bedrock, and AgentCore.
- Proficiency with open-source ML tools including JupyterLab, Spark, and MLflow.
- Deep AWS platform knowledge including S3, Athena, Redshift, Glue, Step Functions, and Lambda.
- Production experience with RAG, prompt engineering, and LLM evaluation.
- Knowledge of vector databases like pgvector or Pinecone.
- Deep Python expertise with core libraries such as scikit-learn, pandas, NumPy, PyTorch, TensorFlow, XGBoost, or LightGBM.
- Working knowledge of Java for service code review and API definition.
- Strong understanding of MLOps, including drift detection, experiment tracking, and deployment patterns.
- Excellent written and verbal communication skills for technical and executive stakeholders.
- Ability to work independently as an independent contractor.
Responsibilities
- Lead the architecture and operation of training infrastructure, model serving, inference pipelines, model registries, feature pipelines, and production integrations.
- Partner with the Data Platform Architect to define ML/AI architecture, engineering standards, tooling strategies, and build-versus-buy decisions.
- Develop practical strategies for RAG, prompt engineering, evaluation, fine-tuning, model serving, and agentic workflows.
- Design scalable ML services and API contracts that integrate cleanly with Java microservices.
- Drive model monitoring, drift detection, reproducibility, experiment tracking, model registries, and cost observability.
- Mentor senior engineers and represent the AI/ML function in cross-functional technical discussions.
- Translate architectural trade-offs into design documents and executive-level recommendations.
View Full Description & ApplyYou'll be redirected to the employer's site
