Codebees - Senior Solution Architect - Artificial Intelligence (12-15 yrs)

Work experience :

- 12+ years of experience in software development, with at least 5+ years focused on AI, machine learning, or related technologies.

- Proven experience in architecting and implementing AI solutions, Generative AI applications, Retrieval-Augmented Generation (RAG), including hands-on development with frameworks like TensorFlow, PyTorch, Keras.

- Experience and Hands Frameworks like Django, Flask, Pyramid, CherryPy and Web2Py

- Extensive experience with cloud platforms, particularly Microsoft Azure, and expertise in deploying AI solutions in cloud environments.

- Extensive experience with generative AI models (i.e., GPT, Stable Diffusion, DALL-E, etc.)

- Deep expertise in building and fine-tuning large language models of OpenAI i.e. GPT 4, GPT 3.5, DALL-E, Whisper, Embeddings, Moderation, and Codex etc.

- Experience in working with Vector Database like Pinecone, Chroma, Qdrant

- Strong knowledge of cloud platforms like Azure, AWS or Google Cloud for AI workloads.

- Strong background in MLOps practices, version control systems, DevOps practices, including CI/CD pipelines, Docker, and Kubernetes.

Additional qualifications : Advanced certifications in AI/ML, Cloud Computing, or Enterprise Architecture are highly desirable.

Skills :

- Deep understanding of AI/ML algorithms, model development, and deployment strategies.

- Proficiency in programming languages such as Python, Java, R, or C#, with a focus on AI/ML libraries and frameworks.

- Experience in natural language processing, computer vision, or multimodal AI applications.

- Strong problem-solving skills, with the ability to design and implement complex technical solutions.

- Excellent communication skills, with the ability to lead technical discussions and collaborate with cross-functional teams.

- Knowledge of ethical AI frameworks and principles.

- Knowledge of enterprise architecture frameworks (TOGAF) and ITIL practices.

Responsibilities :

Solution Architecture and Technical Design :

- Design and architect end-to-end AI solutions, including data pipelines, model development, deployment strategies, and integration with existing systems.

- Define the technical components, services, and libraries required for AI projects, ensuring scalability, security, and performance.

- Lead the selection of appropriate AI frameworks, tools, and platforms (i.e., TensorFlow, PyTorch, Databricks, Azure AI) to meet project requirements.

Hands-On Development :

- Actively participate in the development of AI models, writing code, building algorithms, and deploying models into production environments.

- Collaborate with data scientists and software engineers to implement AI solutions that are robust, scalable, and efficient.

- Ensure that the technical design is aligned with best practices for AI development, including the use of CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployment (Azure).

Technical Leadership :

- Provide technical guidance and mentorship to development teams, ensuring that they follow best practices in AI and software development.

- Review code, design, and architecture to ensure that the solutions meet high standards for quality and security.

- Lead technical discussions in design and implementation phases, making critical decisions that impact the architecture and design of AI solutions.

Component and Service Design :

- Architect and design reusable components, microservices, and APIs that can be leveraged across multiple AI projects within the group

- Develop and maintain libraries of reusable code, tools, and templates that accelerate AI development and ensure consistency across projects.

- Ensure that all components are designed for integration with existing systems, supporting seamless data flow and interoperability.

Research and Innovation :

- Stay up-to-date with the latest advancements in AI, machine learning, deep learning, and cloud computing, bringing new ideas and technologies to the team.

- Experiment with emerging AI technologies, such as Generative AI, Reinforcement Learning, and Neural Architecture Search, to identify their potential applications within the group

- Lead the technical exploration of new AI use cases, developing prototypes and proof-of-concept solutions to validate their feasibility.

Collaboration and Communication :

- Work closely with stakeholders across member firms to understand business needs and translate them into technical solutions.

- Communicate complex technical concepts to non-technical stakeholders, ensuring that they understand the capabilities and limitations of AI technologies.

- Collaborate with external partners, including technology providers and academic institutions, to drive innovation and knowledge sharing.

Security and Compliance :

- Architect AI solutions with a strong focus on security, ensuring that data privacy and protection are built into the design.

- Implement compliance with industry standards and regulations (i.e., GDPR, ISO 27001), ensuring that AI solutions adhere to legal and ethical guidelines