Lead Technical Architect

Key accountabilities and decision ownership:

• Provide design and architecture for the assigned proposals and projects for Data & AI domain.
• Translate client needs into fit-for-purpose Data & AI solutions, leveraging modern cloud platforms (Azure, AWS or GCP).
• Lead complex implementations that deliver measurable outcomes. Architect and scale GenAI chatbots and Agentic AI solutions. Evaluate and select appropriate models, frameworks, and orchestration tools.
• Build reusable architecture artifacts.
• Review the gathered requirements by the Business Analysis; ensure it is in line with the architecture design and apply modifications if needed.
• Align with the technical lead on the Data & AI architecture design to start the implementation process.
• Rework and apply any changes and work in alignment with the development team.
• Support the development team on architecture matters when needed.
• Document all the architecture work.
• Mentor technical architects and senior architects and provide them with support with their projects when needed.
• Support the chief of architecture in enhancing and modifying different engineering review processes.

Must have professional qualifications:

• Bachelor's degree in computer science or equivalent.
• 15+ years of experience in software development including at least 6 years as an architect.
• Experience in architecting and solutioning in Gen AI, Agentic AI, classic ML, and automation space.
• Hands-on mastery of core GenAI frameworks (e.g., LangChain, LlamaIndex) and practical experience with Agentic AI frameworks and concepts (e.g., AutoGen, CrewAI, LangGraph).
• Strong working knowledge and practical experience on at least one major cloud platform (AWS, Azure, GCP), including their AI/ML services.
• Hands on Experience in Prompt engineering, context engineering, RAG pipelines, Supervised/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability.
• Experience in Enterprise-grade RAG-based solutions with LLMs (e.g. OpenAI, Hugging Face, LLaMA, etc.) and vector databases (e.g. Pinecone, Weaviate, FAISS, etc.).
• Proficiency in Python with AI/ML frameworks (PyTorch, TensorFlow).
• Experience with MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes).
• Masters degree in data science is plus.