Senior Data Engineer (AI & MLOps, AWS, Python)

Salt
Tyne and Wear, England
12 months ago
Applications closed

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Senior Data Engineer (AI & MLOps) – Software – Newcastle/Hybrid or Remote

Day rate: £300 – £500 (Inside IR35)


Duration: 6 months


Start: ASAP

My new client is looking for a Senior Data Engineer with expertise in AI, MLOps, and AWS architecture to design and deliver production-grade machine learning pipelines. The ideal candidate will be passionate about bridging the gap between data science experimentation and scalable production systems, driving automation, and enabling faster innovation cycles.

Key Responsibilities

Architect, build, and maintain production-grade ML Ops pipelines to automate deployment, monitoring, and scaling of machine learning models.


Collaborate with data scientists and ML engineers to reduce time-to-production for experiments and prototypes.
Design and optimize data wrangling and transformation workflows using Python.
Leverage AWS cloud services (EC2, S3, Lambda, SageMaker, RDS, DynamoDB, Redshift, etc.) to build robust, scalable, and cost-effective solutions.
Apply AIOps practices to enhance monitoring, automation, and resilience of ML systems.
Implement best practices in data engineering, version control, CI/CD, and infrastructure as code.
Ensure the security, reliability, and compliance of data pipelines and deployed ML solutions.
Mentor junior engineers and contribute to setting technical standards for the team.

Required Qualifications

Proven experience as a Senior Data Engineer, MLOps Engineer, or similar role.


Strong background in data structures, algorithms, and software engineering principles.
Advanced proficiency in Python for data wrangling, pipeline automation, and ML workflows.
Expertise in AWS services, including databases (RDS, DynamoDB, Redshift) and machine learning/AI (SageMaker, AI/ML frameworks).
Hands-on experience with ML pipeline orchestration, CI/CD, and deployment automation.
Deep understanding of ML Ops practices, including monitoring, scaling, and retraining strategies.
Familiarity with AIOps concepts and tools for operational automation.

Preferred Skills

Experience with data science and machine learning model development.


Knowledge of containerization (Docker, Kubernetes, EKS).
Exposure to infrastructure-as-code (Terraform, CloudFormation).
Strong problem-solving, communication, and collaboration skills.

*Rates depend on experience and client requirements

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If you have been watching AI job boards over the past year, one title keeps surfacing again and again: the forward deployed engineer, or FDE. It has gone from a niche term known mainly to Palantir alumni to arguably the hottest role in the entire AI hiring market. Job postings for forward deployed engineers have exploded, salaries have climbed past levels most software engineers will ever see, and the biggest names in AI — OpenAI, Anthropic, Google, Salesforce, Databricks and Palantir — are all competing for the same small pool of talent. So what exactly is an AI forward deployed engineer, why has demand surged so dramatically, and how do you position yourself to land one of these roles? This guide breaks it all down for AI engineers, software engineers and data scientists looking at their next move.