Gen AI Principal

We are seeking a highly skilled and experienced Senior Architect/Consultants to lead our Generative AI Technologies team. The ideal candidate will have a deep understanding of Generative AI, machine learning, and related technologies, along with a proven track record of architecting and implementing innovative solutions. As a Senior Architect/Consultant, you will play a pivotal role in shaping our Generative AI strategy, selecting appropriate models and technologies, and collaborating with cross-functional teams to deliver cutting-edge solutions that meet customer requirements and business objectives.

Primary Skill Set:

• Generative AI Expertise: In-depth knowledge of various Generative AI techniques, including GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and other relevant architectures. Experience with both image and text generation is essential. Conversant with Gen AI development tools like Prompt engineering, Langchain, Semantic Kernels, Function calling. Exposure to both API based and opens source LLMs based solution design.
• Responsible AI: Should have proficient knowledge in Responsible AI and Data Privacy principles to ensure ethical data handling, transparency, and accountability in all stages of AI development. Must demonstrate a commitment to upholding privacy standards, mitigating bias, and fostering trust within data-driven initiatives.
• Machine Learning Mastery: Profound understanding of machine learning principles, algorithms, and frameworks. Able to design and implement models, optimize performance, and manage training pipelines effectively.
• Technical Proficiency: Proficiency in programming languages commonly used in AI development, such as Python, TensorFlow, PyTorch, or similar tools. Experience with cloud platforms (e.g., AWS, Azure, GCP) and distributed computing is advantageous.
• Architecture Design: Ability to design end-to-end Generative AI architectures that encompass data preprocessing, model selection, training pipelines, and deployment strategies. Strong grasp of scalable, reliable, and efficient system design.

Secondary Skill Set:

• Domain Knowledge: Familiarity with the specific industry domain or vertical in which the Generative AI solutions will be applied (e.g., healthcare, finance, entertainment) is beneficial. This enables contextual understanding and tailored solution development.
• Data Engineering: Understanding of data engineering practices, data pipelines, and data management. Proficiency in data preprocessing, cleansing, and transformation for effective model training.
• Communication Skills: Excellent communication and collaboration skills to effectively interface with cross-functional teams, including data scientists, engineers, business stakeholders, and customers. Ability to convey complex technical concepts to non-technical stakeholders.

Roles & Responsibilities:

• Generative AI Strategy: Lead the development of the Generative AI technology roadmap, identifying opportunities, evaluating potential use cases, and proposing innovative solutions that align with business goals.
• Model Selection: Evaluate and select appropriate Generative AI models and techniques based on the specific requirements of each project. Consider factors such as data availability, complexity, and computational resources.
• Architectural Design: Design comprehensive and scalable architectures for Generative AI solutions, considering components such as data preprocessing, model training, deployment, and monitoring.
• Solution Implementation: Collaborate with data scientists and engineers to implement Generative AI solutions, ensuring the seamless integration of models into production environments.
• Performance Optimization: Continuously optimize the performance of Generative AI models, addressing issues related to speed, accuracy, and resource utilization.
• Outcome Review: Assess the outcomes of Generative AI solutions against predefined success criteria. Iterate on models and strategies based on performance metrics and feedback.
• Customer Collaboration: Work closely with customer architecture and business teams to define solution requirements, technical boundaries, and SLAs. Tailor solutions to meet customer needs and address specific challenges.
• Team Collaboration: Collaborate effectively with cross-functional teams, providing guidance and mentorship to junior team members. Foster a collaborative and innovative work environment.
• Industry Awareness: Stay updated with the latest advancements in Generative AI and related technologies. Share insights with the team and incorporate emerging trends into solution design.