Senior Solution Architect

Description :

- Work with a global tech giant on some of the most impactful AI/ML adoption journeys in the industry

- Shape enterprise AI strategy across multiple sectors, from concept to real-world scale

- Collaborate with world-class experts and cutting-edge technologies

- Competitive compensation, high visibility, and career growth in a fast-evolving space

Our client is a global technology leader driving large-scale digital transformation for enterprises worldwide.

The Job :

As a Senior Solutions Architect (AI/ML), you will partner with enterprise customers to design, implement, and scale machine learning and generative AI solutions in the cloud. You will be a trusted advisor, guiding organizations on how to build secure, high-performance, and cost-efficient AI platforms that accelerate business outcomes.

Key Responsibilities :

- Design and implement end-to-end architectures for AI/ML workloads, including data ingestion, model training, deployment, and monitoring.

- Advise on scalable infrastructure for generative AI, foundation models, and data-driven applications.

- Collaborate with data science, product, and engineering teams to accelerate AI adoption across industries.

- Deliver workshops, proof-of-concepts, and executive briefings to showcase AI/ML best practices.

- Support enterprise migrations of AI/ML pipelines from on-premises or legacy systems to the cloud.

- Drive conversations on security, compliance, and responsible AI in large-scale environments.

- Mentor engineering and data teams, helping them operationalize and optimize AI solutions.

Ideal Candidate :

- You have 8-12 years of experience in enterprise IT, with at least 5 years in AI/ML solution architecture or related fields.

- You also have 8+ years of experience in cloud-based solutions, systems, networking, and operating environments, along with strong hands-on expertise in coding, data querying languages (e.g., SQL), and scripting languages (e.g., Python).

- Strong understanding of machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and MLOps practices.

- Hands-on expertise in data pipelines, feature engineering, model deployment, and monitoring at scale.

- Solid background in distributed systems, high-availability design, and cloud-native architectures.

- Experience with generative AI models, LLMs, and integration into enterprise applications is a strong plus.

- Strong communication and stakeholder engagement skills; able to influence both executives and technical teams.

- Curiosity and a continuous learning mindset to stay ahead in the fast-evolving AI/ML landscape.