AI Architect at Qode

, , India -

Full Time

Start Date

Immediate

Expiry Date

03 May, 26

Salary

0.0

Posted On

02 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Architecture Design, MLOps Frameworks, Azure ML, AWS SageMaker, Solution Blueprints, Model Governance, Data Ingestion, Feature Stores, CI/CD, Kubernetes, Docker, Python, Cloud-Native Services, Security Compliance, Technical Leadership, Generative AI

Industry

Software Development

Description

AI ArchitectLocation: To be determinedWorkplace Type: Hybrid/Remote About the RoleWe are seeking an experienced AI Architect to design and govern end-to-end AI and ML architectures across a variety of enterprise use cases (e.g., prediction, personalization, recommendation, anomaly detection, automation). The ideal candidate will operate as a strategic technical leader, defining AI solution blueprints on Azure and AWS and ensuring scalable, secure, and compliant AI systems across products and teams. This role bridges business and technology by translating organizational objectives into robust AI architectures, reference patterns, and platform capabilities that can be reused across multiple domains and product lines. This is an exciting opportunity to shape the future of AI within our organization and contribute to cutting-edge projects. Key Responsibilities Define and own end-to-end AI architecture for multiple enterprise use cases, including data flows, model lifecycle, and serving patterns across Azure and AWS. Design AI/ML solution blueprints covering data ingestion, feature stores, training pipelines, model registry, deployment, monitoring, retraining, and decommissioning. Establish and standardize MLOps frameworks (MLflow/Kubeflow/Airflow, Docker, Kubernetes) and reference implementations that AI/ML engineers and data scientists can reuse across teams. Collaborate with product, data, and business stakeholders to identify AI opportunities, shape solution options, and align AI architectures with business and non-functional requirements (scalability, reliability, cost, latency). Define and enforce governance for model approval, explainability, versioning, drift monitoring, and compliance with data privacy and regulatory requirements relevant to the organization. Guide the selection and integration of cloud-native services (Azure ML, AWS SageMaker, Lambda, EC2, S3, Azure Functions, API gateways, monitoring stacks) into cohesive AI platforms. Work closely with security, compliance, data, and enterprise architecture teams to ensure AI solutions meet standards for security, resilience, observability, and cost optimisation. Provide technical leadership and mentoring to AI engineers, ML engineers, and data scientists on best practices in AI architecture, MLOps, and cloud-native design. Create and maintain architectural artefacts: high-level designs, detailed solution diagrams, standards, and documentation for AI platforms and reference solutions. Required Skills & Qualifications Proven experience designing and leading AI/ML solution architectures in production across multiple projects or products. Ability to translate business use cases into architectural blueprints, roadmaps, and reusable platform components. Strong understanding of ML techniques (supervised, unsupervised, basic deep learning) and their productionisation for real-world use cases. Hands-on experience defining MLOps strategies using MLflow, Kubeflow, Airflow, Docker, and Kubernetes for large-scale deployments. Expert knowledge of Azure ML and AWS SageMaker, plus core services (S3, EC2, Lambda, Azure Functions, identity, networking, monitoring). Experience designing multi-environment (dev/test/prod) AI platforms with robust CI/CD, model promotion, and rollback strategies. Understanding of data privacy, security, and responsible AI principles (fairness, transparency, explainability, auditability). Experience defining policies and controls for model governance and risk management. Strong Python skills and familiarity with common ML libraries (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch). Solid grounding in CI/CD (GitHub Actions, Azure DevOps, AWS CodePipeline) and monitoring/logging solutions (Prometheus, Grafana, or cloud-native equivalents). 7–10 years overall experience (minimum 5+ years in AI/ML, with strong MLOps and cloud experience). Additional InformationPreferred Qualifications include prior experience as an AI Architect, ML Architect, or AI Solutions Architect in an enterprise environment. Certifications such as Azure Solutions Architect Expert, Azure AI Engineer Associate, AWS Certified Machine Learning – Specialty, or equivalent architecture credentials are also highly desirable. Exposure to generative AI and LLM architectures, including RAG, orchestration frameworks, and fine-tuning strategies is a plus. A demonstrated track record of leading cross-functional AI initiatives from concept through architecture, implementation, and scaling is also preferred.At Donyati, we don’t just deliver solutions, we build the future of intelligent business transformation. You’ll join a culture that values curiosity, thought leadership, and impact. Here, you’ll have the autonomy to define strategy, shape teams, and influence enterprise transformation at scale, while staying connected to the technology you love. Lead the practice. Shape the vision. Stay hands-on with impact. We offer competitive compensation and benefits packages, including health insurance, paid time off, and professional development opportunities. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Responsibilities

The AI Architect will design and govern end-to-end AI and ML architectures for enterprise use cases, defining solution blueprints primarily on Azure and AWS. Key duties involve establishing standardized MLOps frameworks and ensuring AI systems are scalable, secure, and compliant across products and teams.