Solutions Architect (Machine Learning)

  • Full Time
  • England

Website Technical Resources

Technical Resources work with thousands of leading Fire & Security, Telecoms and Fibre businesses and candidates to provide a flexible, dedicated, and tailored recruitment experience that delivers high-quality results time after time.

Solutions Architect (Machine Learning)
Location: Hybrid (Mainly London based – ad-hoc on-site)
Duration: 6 Months rolling
Rate: £700pd to £900pd (Negotiable – please tell us what you are looking for)

About The Job
As a Machine Learning Solutions Architect, you will support our Sales and Engineering teams to incubate, pilot, and deploy industry leading AI/ML and Generative AI technology with AI natives, large enterprises, SMEs along with AI startups and scale-ups. You will help customers innovate faster with solutions using the various cloud providers’ infrastructure including bespoke hardware and AI accelerators.

In this role, you will identify, assess and develop Generative AI and AI/ML applications by applying key industry frameworks, techniques, and methodologies to solve problems. You will help customers leverage AI within their overall cloud strategy by helping run benchmarks for existing models, finding opportunities to use new models, developing migration paths, and helping to analyze cost to performance. Youll work closely with internal and external Cloud AI teams to remove roadblocks and shape the future of FrontRunner AI service offerings. You will navigate ambiguity, troubleshoot and find solutions, and learn quickly in a rapidly changing technology space.

Our AI Solutions Architects accelerate organizations’ ability to digitally transform their business using the best platforms, talent and expertise. We deliver enterprise-grade solutions that leverage state of the art AI stacks to solve our customers’ most critical business problems.
Minimum qualifications:
• Masters degree in STEM field, or equivalent practical experience.
• Experience in programming language (e.g. Python, Julia), applied machine learning techniques, and using ML frameworks (e.g., TensorFlow, PyTorch).
• Experience in AI approaches and methods (e.g., deep learning, NLP, computer vision, pattern recognition, LLMs, diffusion models, etc.).
• Experience delivering technical presentations and leading business ideation sessions.
Preferred qualifications:
PhD in Computer Science, Engineering, or a related technical field.
• Experience designing and deploying production solutions with the following machine learning frameworks: TensorFlow, PyTorch, JAX.
• Experience with training and deploying open source LLMs, such as Mixtral, Llama2, Falcon.
• Experience training and fine tuning models in large-scale environments (e.g., image, language, video, recommendation) with accelerators (GPUs & ASICs).
• Experience with CI/CD solutions in the context of MLOps and LLMOps including automation with Infrastructure as Code (IaC)
• Experience with distributed training and optimizing performance of multimodal LLMs versus costs.
• Experience in systems design with the ability to architect and explain data pipelines, machine learning pipelines, machine learning training and serving approaches.
• Knowledge of and hands-on experience with GCP, AzureML and AWS Sagemaker & Bedrock cloud services.
• Be a trusted advisor to our customers by understanding the customer’s business process and objectives. Architect AI-driven business solutions, spanning data, AI models, and infrastructure, and work with our top talent to create and deliver full stack AI solutions.
• Demonstrate how FrontRunner AI is differentiated by working with customers on PoCs, demonstrating AI features, tuning custom models, optimizing model performance, profiling, and benchmarking. Troubleshoot and find solutions to issues with training/serving models in cloud environments.
• Build technical assets such as scripts, templates and reference architectures to enable our customers and internal teams. Work cross-functionally to influence strategy and product direction at the intersection of infrastructure and AI/ML by advocating for a wide range of customer requirements and use cases.
• Coordinate regional field AI Engineers and Solutions Architects with leadership and work closely with product and partner organizations on external enablement activities. Travel as needed.

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