Senior MLOps / Machine Learning Engineer: LLMs & Agentic AI

Senior MLOps / Machine Learning Engineer: LLMs & Agentic AI

Senior MLOps / Machine Learning Engineer: LLMs & Agentic AI

About Data Reply:
Data Reply is the Reply Group company offering a broad range of analytics and data processing services. We operate across different industries and business functions, working directly with executive level professionals, enabling them to achieve meaningful outcomes through effective use of data. We find that one of the biggest problems experienced by our clients today is being overwhelmed with the amount of data that they face and not knowing how to leverage it to their advantage. The vast landscape of available technology stacks and models means that choosing the right ones can be a daunting task. Most companies know that their data is valuable, and that they should be making the most out of it to stay competitive, but often don't know where to begin or what to prioritise. At Data Reply, we pride ourselves on helping clients make the right decisions to build their data strategy. With our consultants' expertise, we map the right technologies to meet our clients' business needs. We deal in bespoke solutions, and offer in house training to ensure that our clients realise the full value of their big data solution. www.data.reply.com

Role Overview:
As a Senior MLOps / ML Engineer at Data Reply, you will take ownership of architecting and deploying ML and GenAI solutions. You'll be hands-on at every stage - from proof-of-concept through production - and you'll help mentor junior AI engineers. A particular focus will be on deploying large-language models (LLMs) and AI agents at scale, integrating them with enterprise workflows, and ensuring repeatable, cost-efficient AWS architectures.

Responsibilities:

• Leading solution workshops to design scalable ML systems on AWS using services like VPC, IAM, SageMaker Studio, Lambda, and EKS
• You'll build CI/CD pipelines using GitHub Actions, Jenkins, and AWS CodePipeline for deploying traditional ML, GenAI models, and AI agents
• Deploying LLMs (e.g., via Huggingface) and construct AI agent workflows using tools like LangChain, LangGraph, and custom orchestrators
• Your expertise will help reduce cloud costs with GPU acceleration, auto-scaling, and spot instances
• To implement model lifecycle tools (MLflow, SageMaker Registry), performance dashboards, alerts, and automated retraining pipelines
• Connecting ML models to client systems using APIs, Kafka, and build agent workflows with vector databases (Pinecone, Weaviate)
• You'll enforce secure, compliant, and ethical practices-VPC design, IAM policies, data encryption, and adherence to GDPR
• You'll be a trusted advisor and mentor, presenting technical solutions, managing expectations, and guiding junior team members

• University degree in Computer Science, Mathematics or in a directly related field (2.1 min grade)
• 3+ years in MLOps/ML Engineering experience, plus 5+ years in Python software development or data science

• Skilled in SageMaker (training, endpoints, pipelines), Lambda, Step Functions, S3, and CloudWatch
• Proficiency with Terraform or AWS CDK, Docker, and Kubernetes (EKS/Fargate)
• Experienced with MLflow (or alternatives), GitHub Actions, Jenkins, AWS CodePipeline, and automated testing
• You've got hands-on experience with deploying LLMs and building AI agents using LangChain or custom frameworks
• Strong background in building data pipelines with Airflow/dbt and managing features via Feast or similar tools
• You have experience building dashboards with CloudWatch/Prometheus/Grafana and implementing data validation with Great Expectations
• It would be beneficial to have exposure to consulting/presales, MCP deployment, Databricks, and AWS ML Specialty certified

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