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What we offer:

Group Summary:

The GenAI Solution Architect is a full-stack developer who will act as the technical visionary and hands-on leader, responsible for designing, building, and delivering comprehensive GenAI software solutions that create business value. You will work closely with stakeholders—product managers, data scientists, solutions architects, UX/UI designers, and operations teams—to convert strategic goals into scalable architectures, utilizing both generative and traditional AI models alongside robust front-end and back-end systems. Your primary responsibilities include defining system requirements and roadmaps, leading development across cloud-native microservices and user-facing applications, ensuring code quality and performance, and implementing best practices in security, observability, and CI/CD. In this role, you will also mentor engineering teams, promote emerging AI capabilities, and continuously improve solution patterns to boost innovation and sustain our competitive advantage.

Major Responsibilities

• Define and document end-to-end solution architectures for GenAI services and full-stack applications, including high-level diagrams, data flows, and technology stack decisions

• Architect and manage scalable GenAI solutions using Azure services (e.g., AI Foundry, Apps Function, Storage Account, Cosmos DB, Azure AI search, Azure AI Services, AKS, VMs, Container Registry, etc.).

• Collaborate with product management and business stakeholders to refine requirements, prioritize features, and ensure alignment with strategic goals

• Lead design and implementation of microservices, APIs, and user interfaces, leveraging cloud-native platforms (e.g., Azure or AWS) and container orchestration (e.g., Kubernetes, Docker)

• Select, evaluate, and integrate generative AI models—fine-tuning, testing, and optimizing for production performance and cost efficiency.

• Establish and enforce engineering best practices around code quality, automated testing, CI/CD pipelines, and infrastructure-as-code (e.g., Terraform, ARM templates)

• Implement robust security, observability, and governance patterns, including identity management, data encryption, logging, and monitoring

• Mentor and coach development teams on architectural patterns, coding standards, and GenAI models deployment techniques.

• Drive proof-of-concepts and pilot projects to validate new technologies, tools, and frameworks, then translate learnings into reusable solution patterns.

• Evangelize GenAI capabilities across the organization—conduct technical workshops, brown-bags, and architecture reviews to build internal expertise.

• Ensure best practices in security, monitoring, and compliance within the cloud infrastructure.

• Continuously assess emerging trends in GenAI, full-stack development, and cloud infrastructure to recommend enhancements and maintain a competitive technology roadmap

Knowledge and Education

• Bachelor’s degree in computer science, Software Engineering, Electrical Engineering, or a closely related technical field

• Master’s degree in Artificial Intelligence, Machine Learning, Data Science, or equivalent (preferred)

Work Experience

• Minimum of 10 years of hands-on software development and architecture design

• At least 3 years leading end-to-end GenAI solution delivery in production environments

• 5+ years of full-stack application architecture, with proven expertise in cloud-native microservices.

Skills and Competencies Required to Perform the Job

• Proven experience with LLMs and NLP frameworks (e.g., Hugging Face, OpenAI, or Anthropic models).

• Implement LLMOps practices to streamline model deployment and CI/CD workflows.

• Cloud-Native Architecture: Experience designing scalable microservices and serverless solutions on Azure or AWS (e.g., Kubernetes, Docker)

• Full-Stack Development: Experience in hands-on coding in .Net, Python, or Java/Kotlin, and JavaScript/TypeScript with frameworks like React, Angular, and Node.js

• GenAI Expertise: Experience in selecting, fine-tuning, deploying, monitoring, and optimizing ML and generative AI models in production

• Data Engineering: Experience in building robust ETL/ELT pipelines, working with SQL/NoSQL stores and real-time streams (e.g., Kafka, Azure Event-Hub)

• Infra-as-Code & CI/CD: Experience in automating provisioning (e.g., Terraform, ARM template) and delivery pipelines (e.g., Jenkins, GitHub Actions) with automated testing

• Security & Observability: Experience in implementing IAM, encryption, secrets management, and logging/tracing/metrics (e.g., Azure Monitor, Prometheus, Grafana, ELK)

Working Conditions and Environment

• No Travel

Any Additional Information

• Excellent English in both spoken and written.

• Excellent problem-solving and decision-making abilities.

• Strong communication skills and the ability to lead cross-functional teams.

• Passion for mentoring and developing engineers.

Awareness, Unity, Empowerment: