At a Glance
- Tasks: Lead the deployment of cutting-edge ML solutions for enterprise clients.
- Company: Join a forward-thinking tech company focused on innovation and client success.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic role with opportunities to influence and build strategic partnerships.
- Why this job: Make a real impact by driving ML adoption in diverse environments.
- Qualifications: 2+ years in ML engineering or consulting with strong technical skills.
The predicted salary is between 60000 - 80000 £ per year.
Requirements
- Education: Bachelor's degree in Computer Science or a related field.
- Experience: 2+ years of experience in a technical, customer-facing role such as Forward Deployed Engineer, or as a Software/ML Engineer with consulting experience.
- ML Engineering & Training Expertise: Experience in the Machine Learning lifecycle (training, optimization, deployment), with a proven ability to lead and execute complex model deployments in production environments.
- Forward Deployed/Consulting Background: Proven track record working within or closely alongside client engineering teams to successfully deploy and integrate complex, high-performance software, involving cloud or on-premise ML workloads.
- Technical & MLOps Knowledge: Understanding of modern ML frameworks, programming languages including Python, and deployment technologies (Docker, Kubernetes, cloud services like SageMaker/Vertex AI/Azure AI).
- Value-Driven Influence: Demonstrated ability to influence senior technical leaders and lead engineers, translating complex model performance and system architectures into clear, tangible business value and deployment assurance.
What the job involves
- You will be the main technical architect responsible for how our most strategic enterprise clients and partners implement and deploy our machine learning solutions.
- As one of our first group of Forward Deployed ML Engineers, you will establish our ML solutions for organizations concerned with the quality, security, performance, and cost of coding models.
- You will leverage your deep ML expertise and technical skills to ensure successful, production-grade implementations, ultimately driving rapid market adoption through proven on-site technical success and client satisfaction.
- End-to-End Ownership: Proactively engage with client or partner teams in Research, Engineering, Data Science, MLOps, Infrastructure to understand their business and technical requirements. With our internal R&D team in the loop, design specific implementations that you will integrate, optimize, and productionize within the client’s existing or greenfield systems as well as transferring technical knowledge to client teams when applicable.
- Subject Expert: Stay up-to-date with the latest LLM capabilities and implementation patterns, you are learning driven. You will need to explain complex technical details and concepts to both technical and non-technical audiences.
- Influence Model Training & Tuning: Represent our core R&D team on-site, leading technical engagement with modern techniques covering all stages of model training using complex, proprietary client data. Ensure architecture is aligned with and optimized for specific constraints (e.g. GPU types, air-gapping).
- Develop Deployment Strategy: Define and execute a global technical strategy for integrating our ML solutions into diverse client environments, ensuring compliance with sector-specific data security standards and performance SLAs. Based on your implementations, build reusable playbooks and libraries that will accelerate yourself and others.
- Building Relationships: Operate autonomously and with agency to build strong relationships with clients, create strategic technical partnerships and drive high-value, referenceable production deployments.
- Serve as Internal Expert: Act as the primary internal consultant, advising product, research, and sales on real-world client infrastructure limitations, performance bottlenecks, and emerging technical standards necessary for product success.
On-Site ML Deployment Architect employer: SonarSource
As an On-Site ML Deployment Architect, you will thrive in a dynamic work environment that champions innovation and collaboration. Our company prioritises employee growth through continuous learning opportunities and a supportive culture that values your expertise in machine learning. Located in a vibrant tech hub, we offer competitive benefits and the chance to work closely with leading enterprise clients, ensuring your contributions have a meaningful impact on their success.
StudySmarter Expert Advice🤫
We think this is how you could land On-Site ML Deployment Architect
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the industry. Attend meetups, webinars, or conferences related to ML and tech. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those involving deployment and integration. This will give potential employers a taste of what you can do and how you tackle real-world problems.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss your experience with ML frameworks, deployment technologies, and how you've influenced teams in past roles. Practice makes perfect!
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining us. Tailor your application to highlight your relevant experience and how you can contribute to our mission in ML deployment.
We think you need these skills to ace On-Site ML Deployment Architect
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the On-Site ML Deployment Architect role. Highlight your experience in ML lifecycle, technical roles, and any consulting work you've done. We want to see how your skills match up with what we're looking for!
Showcase Your Projects:Include specific projects where you've successfully deployed ML models or worked closely with client teams. We love seeing real-world examples of your expertise, so don’t hold back on the details!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about ML deployment and how you can bring value to our team. We want to know what makes you tick and how you can help us succeed.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining the StudySmarter family!
How to prepare for a job interview at SonarSource
✨Know Your ML Lifecycle
Make sure you can confidently discuss the entire machine learning lifecycle, from training to deployment. Be prepared to share specific examples of how you've successfully led model deployments in production environments.
✨Showcase Your Technical Skills
Brush up on your knowledge of modern ML frameworks and programming languages, especially Python. Familiarise yourself with deployment technologies like Docker and Kubernetes, as well as cloud services such as SageMaker or Azure AI, so you can speak fluently about them during the interview.
✨Demonstrate Client Engagement Experience
Highlight your experience working closely with client engineering teams. Share stories that illustrate how you've influenced technical leaders and translated complex concepts into business value, showcasing your ability to build strong relationships.
✨Stay Current and Be Learning-Driven
Keep up-to-date with the latest developments in ML and be ready to discuss recent trends or techniques. Show your passion for continuous learning and how it has helped you adapt and innovate in previous roles.
