GenAI Architect

Your Role :

You will be responsible for architecting scalable and modular Generative AI solutions, enabling seamless integration of AI capabilities into enterprise and embedded products. You will lead the development of reusable assets, proof of concepts, and production-ready solutions while defining the roadmap for next-generation AI technologies and platforms.

In this role, you will:Location:

• Looking for professionals with 8–15 years of experience in architecting and implementing scalable, secure, and modular Generative AI solutions across enterprise environments.
• Design and develop AI assets, accelerators, and Proof of Concepts (PoCs) and industrialize them for project deployments.
• Define and drive the roadmap for emerging AI technologies, tools, and frameworks.
• Collaborate closely with business stakeholders, delivery teams, and engineering groups to identify and implement AI-driven opportunities.
• Build and optimize Agentic AI workflows using modern orchestration frameworks.
• Implement Retrieval Augmented Generation (RAG) architectures with vector databases and knowledge retrieval systems.
• Support prompt engineering, prompt orchestration, model integration, model fine-tuning, observability, and AI toolchain automation.
• Integrate foundation models and AI services into enterprise and embedded product ecosystems.
• Develop cloud-native microservices and APIs to support AI applications and workflows.
• Establish governance, compliance, security, and monitoring frameworks for AI deployments.
• Partner with data scientists and ML engineers to operationalize AI and machine learning models in production environments.

Your Profile

Mandatory Skills

• Strong experience with Agentic AI frameworks and multi-agent architectures.
• Expertise in Generative AI, Large Language Models (LLMs), and NLP technologies.
• Hands-on experience with orchestration frameworks such as LangChain, Semantic Kernel, CrewAI, AutoGen, or similar platforms.
• Strong knowledge of Retrieval Augmented Generation (RAG) techniques and vector databases.
• Experience integrating LLMs such as OpenAI, Claude, Bedrock, Gemini, or equivalent AI services.
• Proficiency in Python and at least one additional language such as Rust or .NET.
• Strong experience in microservices architecture, API management, and distributed systems.
• Expertise in cloud platforms such as Azure and AWS.
• Experience with model integration, fine-tuning, evaluation, and observability frameworks.
• Strong understanding of data flow architecture, security, compliance, and governance requirements.

Preferred Skills

• Experience integrating AI capabilities into embedded or edge-based products.
• Familiarity with MLOps, CI/CD pipelines, and AI platform engineering.
• Exposure to Kubernetes, containerization, and cloud-native deployment patterns.
• Knowledge of AI governance, responsible AI practices, and regulatory compliance.
• Strong stakeholder management and solution consulting skills.