Machine Learning Solution Architect

What’s the role?

As an ML Solution Architect, you will play a pivotal role in designing and implementing enterprise-grade AI/ML solutions that align with business objectives and technology strategy. This is a Senior position requiring a blend of technical depth, architectural vision, and business acumen. You will define frameworks, guide engineering teams, and ensure scalability, security, and compliance across ML deployments.

In this role, you will have the opportunity to…

- Architect end-to-end AI/ML solutions including data pipelines, model deployment, cloud-native integrations, and enterprise-grade infrastructure.
- Lead technical design reviews, mentor engineering teams, and evaluate new AI/ML technologies (LLMs, RAG, AutoML).
- Drive POCs and convert prototypes into production-grade models using Tensor Flow, PyTorch, Lang Chain, and modern cloud platforms (AWS, GCP, Azure).
- Develop APIs and microservices enabling seamless integration of AI capabilities into products.
- Implement strong MLOps practices—CI/CD for ML, automated retraining, model performance monitoring, and governance.
- Partner with data engineering to ensure robust pipelines, high-quality data, lineage, and feature engineering standards.
- Collaborate with product, engineering, consulting partners, and business stakeholders to align solutions with strategic goals.
- Advocate for responsible AI practices, explainability, ethical deployment, and build reusable frameworks/architectures.

Qualifications Required…

- Experience:

15+ years of development experience, with at least 7+ years in AI/ML and 4+ Years in ML solution architecture.
- Strong experience with enterprise architecture frameworks, design patterns, cloud‑native architectures, and modern integration approaches.
- Proven ability to design, build, and deploy systems using diverse model types (language, vision, traditional ML) and the supporting infrastructure.
- Skilled across multiple programming languages, major cloud platforms (AWS/GCP/Azure), and architectural modelling tools.
- Background in consulting or prior roles with cloud providers (AWS/GCP), or a senior ML engineer/solution architect who has grown into a broader architectural role.

Technical Expertise:

- AI/ML Tools:
Tensor Flow, PyTorch, Scikit-Learn, Keras, H2O, LLMs, RAG.
- Cloud Platforms: AWS, Azure, GCP with strong knowledge of cloud-native ML services.
- MLOps: CI/CD for ML, Docker, Kubernetes, MLflow, Kubeflow.
- Data Engineering: ETL pipelines, big data frameworks (Spark, Hadoop).
- Programming:
Python, SQL; familiarity with API development and microservices.