Description
Job Title:ML Ops EngineerInside IR35
Location:London Paddington
Department/Practice:
Computer Consultancy
Job Purpose and primary objectives:
We are a data driven organisation, building scalable platforms and processes that accelerate how we use machine learning to solve difficult customer and business problems.
As part of the Back End Engineering Fellowship, you'll be working with API engineering, Data science and platform engineering teams to help drive our machine learning operations, increase our Machine Learning Test Score and platform maturity levels.
You will be deploying, measuring and maintaining our models and continuously improving our production workload ML pipelines and processes in the relentless pursuit of mastering the themes of automation and reliability.
Key responsibilities (please specify if the position is an individual one or part of a team):
- Produce high-quality, reliable machine learning model pipelines and continuously deliver and test them with a high degree of automation in the cloud.
- Reduce manual steps and increase the velocity of experiments by reducing the time between data collection, model training, deployment and feedback cycles.
- Address infrastructure provisioning requirements and manage cloud resources and services.
- Own complex tasks in the backlog and deliver them routinely with no significant issues.
- Demonstrate responsibility for the entire team's objectives.
- Progress work consistently without significant need for support from more senior colleagues.
- Solve problems collaboratively, communicating decisions through tech-talks and white-boarding sessions.
- Deliver high-quality software design through proficiency in design patterns, algorithms, and data structures.
- Help other engineers to produce clean, quality code through code reviews and pair programming.
- Engage with product colleagues to improve value for the customer and to understand ambiguous requirements.
- Approach and contribute to product planning and roadmap with an agile mindset.
- Demonstrate and champion pragmatism in terms of commercial delivery and software quality.
- Support the technical platform out of hours as a member of the support rota.
- Evangelise technology, values and ways of working within the team and wider software engineering community.
- Contribute actively to Fellowships and Orchestras with a focus on positive momentum.
- Coach, mentor and develop lesser experienced engineers.
- Lead and take responsibility for initiatives to support senior colleagues.
- Assist with recruitment and new starter onboarding activities.
Key Skills/Knowledge:
Azure Cloud Native Services
Databricks, Kubeflow, Azure ML, MLFlow
Azure Data Lake Store, Azure Data Factory
Kubernetes, Docker, Python microservices
Kafka, MongoDB, Redis
Concourse, Github Actions, Azure Devops
Terraform, New Relic, PagerDuty
Experience required:
6+ years' experience as listed above.
Duration of the Assignment:
6months +
Person Specification:
ie Negotiating, client facing, communication, assertive, team leading/team member skills, supportive.
. Excellent communication skills and be able to communicate technical concepts in a clear and concise manner.
. Ability to manage multiple Stakeholder Management
Working together
You will be working closely with other Software Engineers, Data Scientists and Product Managers to collaboratively solve problems with a vast amount of data. There will be day-to-day interactions with Scrum Masters, who you'll work with to constantly evolve your team's ways of working. You will also interact with UX/UI Designers, analytics experts and other engineering platform teams.
As a member of M&S Software Engineering, you will also have the opportunity to work with and learn from other engineers and specialists in the organisation via our teams (Squads), sets of squads (Tribes), communities of practices (Fellowships), strategic assemblies (Orchestras) or special interest groups (Guilds).
What you need to succeed
Mission Critical Experiences
Experience and skills with much of the area tech stack.
The ability to validate and audit data sources and ensure consistently high data quality and integrity.
Expertise with distributed version controls systems like Git.
Proficiency with TDD and/or BDD and non-functional testing.
Experience of DevSecOps: you build it, you run it.
Advocate and experience with Continuous Integration, Continuous Delivery and Continuous Testing.
Knowledge of Software approaches like Domain Driven Design and Event Driven Architectures.
Awareness of security best practice, ML security risks, privacy concerns and GDPR/PCI compliance.
Experience with cloud engineering.
Experience with containerisation like Kubernetes and Docker.
Ability to coach and develop lesser experienced engineers.
Proficiency with documenting software design, technical decisions and recommendations.