Design, build and deploy scalable AI and Machine Learning solutions across Data Engineering, DevOps and Machine Learning. The role focuses on Generative AI, traditional ML pipelines, data platforms and reliable, efficient and secure AI systems.
Experience:
Intermediate-Senior level.
Minimum 5 years' professional experience.
At least 2 years' experience within AI/ML.
1 year in Generative AI highly desirable.
Strong Python, AI/LLM, ML engineering, MLOps, Big Data and cloud experience.
Key Requirements
Design and implement end-to-end AI/ML pipelines from data ingestion through model deployment and monitoring.
Build and optimise Generative AI applications using RAG and LLM frameworks.
Develop scalable data processing workflows using modern Big Data tools.
Implement MLOps practices for model lifecycle management, versioning and automated deployment.
Collaborate with data scientists and stakeholders to translate business requirements into technical solutions.
Ensure data quality, governance and security across AI and data platforms.
Expert-level Python with OOP, design patterns and asynchronous programming.
AI frameworks: LangChain, Langflow and AutoGen; ability to build agents and orchestrate LLM workflows.
Generative AI: RAG, Vector Search using Pinecone, Chroma or Milvus, and advanced Prompt Engineering.
MLOps: MLflow, Feature Stores and Model Serving using TFServing, TorchServe or KServe.
Big Data: PySpark, Spark SQL and Delta Lake.
Git workflows and CI/CD using GitHub Actions or Azure DevOps.
Workflow orchestration using Apache Airflow, Databricks Workflows or DLT.
Modern data architecture including Medallion Architecture and ETL/ELT data modelling.
Data governance using Unity Catalog, including lineage, access controls and security policies.
Real-time processing using Apache Kafka, AWS Kinesis or Spark Structured Streaming.
Data quality checks and validation using Great Expectations or Deequ.
BI and visualisation using PowerBI, Tableau or Databricks SQL Dashboards.
AWS: S3, IAM, VPCs, subnets, security groups and AWS networking.
AWS AI/ML services: Amazon Bedrock and Amazon SageMaker.
AWS Kinesis for real-time data ingestion and processing.
Azure: ADLS Gen2, Azure OpenAI Service, Azure AI Search, ARM templates/Bicep and Azure monitoring.
Azure networking and security: VNETs, Private Endpoints, Managed Identities and Key Vault.
Databricks: Mosaic AI, MLflow, Unity Catalog, Jobs, Workflows, DLT, Spark SQL/PySpark optimisation, Databricks CLI and REST APIs.
Certifications advantageous: NVIDIA Certified Associate or similar GenAI certifications; [URL Removed] specialised courses; AWS Certified Machine Learning - Specialty; AWS Solutions Architect - Associate; Microsoft Azure AI Engineer Associate (AI-102); Microsoft Azure Data Scientist Associate (DP-100); Databricks Certified Machine Learning Professional; Databricks Certified Generative AI Engineer Associate; Databricks Certified Data Engineer Professional.
Python back-end engineers with Python, API development and system architecture experience may be considered for training, with gaps in AI frameworks, ML concepts, Spark and Vector DBs.
Data Engineers with Spark, SQL, pipelines and cloud infrastructure experience may be considered for training, with gaps in Model Serving, MLflow, LLM architectures and prompt engineering.
Data Scientists with statistics, model building and experimentation experience may be considered for training, with gaps in software engineering, CI/CD, testing and production deployment.
Should you meet the requirements for this position, please email your CV to [Email Address Removed]. You can also contact the IT team on [Phone Number Removed]; or visit our website at [URL Removed] NOTE: When replying to the advert, also include the reference number in the subject line. Correspondence will only be conducted with short listed candidates. Should you not hear from us within 3 days, please consider your application unsuccessful.
Desired Skills:
AI Engineer