Generative AI Technical Architect

Experience : 10-14 years.

JD :

Generative AI Architect will play a pivotal role in designing, developing, and implementing advanced generative AI solutions that drive significant business impact for our clients.

This role offers the exciting opportunity to work at the forefront of AI innovation, leveraging the power of AI.

Role Overview :

The Generative AI Architect will be responsible for the end-to-end architecture, design, and deployment of scalable and robust generative AI systems.

This includes conceptualizing solutions, selecting appropriate models and frameworks, overseeing development, and ensuring the successful integration of generative AI capabilities into existing and new platforms.

You will work closely with business stakeholders to translate complex requirements into high-performance AI solutions.

Key Responsibilities :

- Architect and Design Generative AI Solutions : Lead the architectural design of generative AI systems, including model selection (LLMs), RAG and fine-tuning approaches.

- Azure AI Expertise : Design and deploy scalable AI solutions leveraging a comprehensive suite of Azure AI services.

- Python Development : Write clean, efficient, and maintainable Python code for data processing, automation, and API integrations.

- Model Optimization and Performance : Optimize generative AI models for performance, scalability, and cost-efficiency.

- Data Strategy : Design data architectures and pipelines to ingest, process, and prepare data for generative AI model training and inference, utilizing Azure data services.

- Integration and Deployment : Oversee the integration of generative AI models into existing enterprise systems and applications.

- Implement robust MLOps practices, CI/CD pipelines (e.g., Azure DevOps, GitHub, Jenkins), and containerization (Docker, Kubernetes) for seamless deployment.

- Technical Leadership & Mentorship : Provide technical leadership and guidance to development teams, fostering best practices in AI model development, deployment, and maintenance.

- Research and Innovation : Stay abreast of the latest advancements in generative AI technologies, research methodologies, and industry trends.

- Drive proof-of-concepts (PoCs) and pilot implementations for new AI capabilities.

- Collaboration and Communication : Collaborate effectively with cross-functional teams, including product managers, data scientists, software engineers, and business analysts, to ensure AI solutions align with business goals and deliver tangible value.

- Articulate complex technical concepts to non-technical stakeholders.

Required Skills and Qualifications :

- Minimum of 10-16 years of experience in IT, with at least 3+ years specifically focused on Gen AI architecture and development.

Technical Proficiency :

- Deep expertise in Azure Cloud Platform and its AI services.

- Strong proficiency in Python and relevant AI/ML libraries (e.g., TensorFlow, PyTorch, Hugging Face, LangChain, LlamaIndex).

- Solid understanding of large language models (LLMs), transformer architectures, diffusion models, and other generative AI techniques.

- Hands-on experience with prompt engineering, RAG pipelines, and vector databases (e.g., Pinecone, Weaviate, Chroma).

- Experience with MLOps, CI/CD, and deployment tools (Azure DevOps, GitHub, Kubernetes).

- Familiarity with RESTful API design and development.

Architectural Principles : Strong understanding of cloud architecture, microservices architecture, and design patterns.

Problem-Solving : Excellent analytical and problem-solving skills with the ability to think critically and creatively.

Communication : Exceptional communication and interpersonal skills, with the ability to convey complex technical concepts clearly to both technical and non-technical audiences.

Team Player : Ability to work collaboratively in a fast-paced, agile environment and lead projects with multiple stakeholders.

Desirable :

- Relevant Azure AI certifications.

- Experience with other cloud platforms (AWS).

- Experience with fine-tuning LLMs for specific use cases.

- Contributions to open-source AI projects or publications in AI/ML.