Senior Solutions Architect (AI/ML) - Contractual

As a Senior Solutions Architect (AI/ML) at Proximity, your primary responsibilities will include designing and implementing scalable AI/ML-driven solutions, leading cross-functional teams, and ensuring high availability of AI systems. You will drive the end-to-end architecture and implementation tailored for GenAI, setting up ML infrastructure, fostering collaboration, and mentoring team members.

To qualify for the Senior Solutions Architect (AI/ML) position at Proximity, you should have 8+ years of experience in software engineering or architecture. Of this, at least 4 years should involve leading cross-functional GenAI/ML teams and implementing enterprise-scale AI architecture solutions. Proficiency with modern AI frameworks and cloud architectures will also be on the list!

Proximity values continuous learning and growth. As a Senior Solutions Architect (AI/ML), you'll engage in proximity talks with industry experts, gain insights through collaboration, and have access to training opportunities that keep you at the cutting edge of AI and ML. The strong culture of mentorship ensures that you are growing alongside your peers.

In the role of Senior Solutions Architect (AI/ML) at Proximity, you'll work on a variety of projects focused on cutting-edge AI/ML technologies. This includes designing multi-agent systems, architecting RAG solutions, and managing deployment pipelines for real-time applications. You’ll tackle complex challenges across diverse industries, enabling scalable solutions that have a real-world impact.

Working as a Senior Solutions Architect (AI/ML) at Proximity comes with a plethora of benefits, including a competitive salary, the opportunity to work with a world-class team, and a culture that fosters innovation and collaboration. You'll be at the forefront of tech trends, with resources and a network to help you thrive and grow in your career.

Certainly! When addressing your experience with distributed AI systems, highlight specific projects where you've architected or managed such solutions. Discuss your role, the technologies used, and how you ensured reliability and scalability. Show your understanding of distributed training and inference, while also demonstrating your problem-solving skills in complex environments.

In your answer, mention frameworks like BentoML, Ray Serve, or Triton that you have successfully used for model serving. Explain why you choose specific tools based on their performance, scalability, and ease of integration within your projects. Provide examples of how you've implemented these tools to achieve high throughput and low latency in your model-serving architecture.

When discussing your mentoring approach, emphasize the importance of hands-on learning, fostering an open environment, and setting shared goals. Talk about how you have previously guided teams through architectural discussions, encouraging a culture of innovation and providing resources for them to expand their knowledge on advanced AI concepts.

When addressing AI governance and security, highlight the importance of ethical AI usage and data protection. Discuss specific measures you've implemented like access controls, auditing, and ensuring compliance with regulations. Show that you understand the nuances between governance in AI projects and that you keep security at the forefront of your architectural decisions.

Provide a specific example where your decision-making led to a successful outcome. Explain the factors you considered, such as project requirements, stakeholder input, and technical feasibility. Highlight your analytical skills and how your choice affected the project positively, ensuring you emphasize the impact of your leadership in that scenario.

Share strategies that have worked for you in the past, such as using auto-scaling techniques, leveraging serverless architectures, and employing cost analysis tools. Highlight specific experiences where you've seen improvements, and discuss your ability to make data-driven decisions to optimize resources effectively.

Discuss your approach to A/B testing, focusing on your steps to set experiments, define key metrics, and analyze results. Provide insights into how you've ensured statistically significant results and how this testing has led to improved performance or user experiences in AI-driven applications you have managed.

When addressing synthetic data generation, illustrate your familiarity with various tools and methodologies you've utilized. Talk about your understanding of its importance in AI model training, and mention examples where synthetic data helped overcome challenges like data scarcity or privacy concerns.

Emphasize that documentation is crucial in AI architecture for various reasons: it preserves knowledge, aligns stakeholders, and facilitates maintainability. Share your experience in creating comprehensive documentation that has aided teams in understanding complex architectures, and discuss how you've improved processes through clear communication.

Share your insights about emerging trends such as federated learning, explainable AI, or advancements in natural language processing. Discuss how you've kept abreast of these trends and how they might influence future projects, showcasing your forward-thinking approach to AI/ML solutions.