Infosys Consulting is the worldwide management and IT consultancy unit of the Infosys Group (NYSE: INFY), a global advisor to leading companies for strategy, process engineering, and technology-enabled transformation programs.
We partner with clients to design and implement customized solutions to address their complex business challenges, and to help them in a post-modern ERP world. By combining innovative and human-centric approaches with the latest technological advances, we enable organizations to reimagine their future and create sustainable and lasting business value.
A pioneer in breaking down the barriers between strategy and execution, Infosys Consulting delivers superior business value to its clients by advising them on strategy and process optimisation as well as IT-enabled transformation. To find out how we go beyond the expected to deliver the exceptional, visit us at www.infosysconsultinginsights.com Infosys Consulting – is a real consultancy for real consultants.
We have an opportunity available for a GEN AI Architect - Senior/ Principal Consultant to join our Digital Team.
Key Responsibilities:
AI Architecture & Solution Design
• Design scalable and robust AI/ML architectures tailored to client requirements.
• Evaluate and select appropriate AI technologies, frameworks, and platforms.
• Collaborate with data scientists, engineers, and product teams to ensure seamless integration of AI solutions.
• Lead technical discovery sessions and translate business problems into AI use cases.
• Partner with account teams to understand client needs and craft compelling AI solution proposals.
• Deliver technical presentations, demos, and proof-of-concepts (PoCs) to prospective clients.
• Respond to RFPs, RFIs, and technical questionnaires with detailed and accurate information.
• Act as a trusted advisor to clients, guiding them through the AI adoption journey.
Solution Design & Development : Collaborate with stakeholders to turn business challenges into technical solutions underpinned by generative AI, leveraging multi-modal LLMs that encompass voice, text, and vision capabilities, including running models locally.
• Prototyping Tools : Utilize tools such as Gradio and Flutter for rapid prototyping and demonstration of AI solutions, focusing on seamless integration of multi-modal inputs and outputs.
• Performance Tuning & Benchmarking : Optimize and improve the performance of AI solutions by implementing best practices (re-ranking, indexation). Conduct benchmarking to test and validate the effectiveness and efficiency of AI models and solutions, ensuring they meet required standards for both prototypes and full-scale implementations.
• Cloud Infrastructure : Utilize both GCP and AWS cloud stacks for development and deployment, employing Python, Jupyter notebooks, and relevant technologies for managing large-scale multi-modal data.
• Vendor Management : Act as a Subject Matter Expert (SME) in vetting and evaluating vendor solutions to ensure alignment with project goals and the integrity of complex AI implementations, ensuring the effectiveness of multi-modal LLM utilization.
• Guardrails and Ethics : Maintain an awareness of AI/GenAI ethics, guardrails, and principles, ensuring solutions are developed responsibly and ethically, especially in handling sensitive multi-modal data.
• Agile Development : Use Jira for task and project management, ensuring agile methodologies are followed to streamline development processes and enhance collaboration on multi-modal projects.
• Stakeholder Engagement : Engage with stakeholders to ensure alignment of technical solutions with business needs, facilitating feedback and iteration throughout the development process, and tailoring multi-modal capabilities to specific user requirements.
• Continuous Learning & Research : Stay abreast of the latest developments in AI/GenAI technologies, including advancements in multi-modal LLMs, enhancing solutions through cutting-edge research and innovation.
• Solution Design & Development : Collaborate with stakeholders to turn business challenges into technical solutions underpinned by generative AI, leveraging multi-modal LLMs that encompass voice, text, and vision capabilities, including running models locally.
• Prototyping Tools : Utilize tools such as Gradio and Flutter for rapid prototyping and demonstration of AI solutions, focusing on seamless integration of multi-modal inputs and outputs.
• Performance Tuning & Benchmarking : Optimize and improve the performance of AI solutions by implementing best practices in model tuning. Conduct benchmarking to test and validate the effectiveness and efficiency of AI models and solutions, ensuring they meet required standards for both prototypes and full-scale implementations.
• Cloud Infrastructure : Utilize both GCP and AWS cloud stacks for development and deployment, employing Python, Jupyter notebooks, and relevant technologies for managing large-scale multi-modal data.
• Vendor Management : Act as a Subject Matter Expert (SME) in vetting and evaluating vendor solutions to ensure alignment with project goals and the integrity of complex AI implementations, ensuring the effectiveness of multi-modal LLM utilization.
• Guardrails and Ethics : Maintain an awareness of AI/GenAI ethics, guardrails, and principles, ensuring solutions are developed responsibly and ethically, especially in handling sensitive multi-modal data.
• Agile Development : Use Jira for task and project management, ensuring agile methodologies are followed to streamline development processes and enhance collaboration on multi-modal projects.
• Stakeholder Engagement : Engage with stakeholders to ensure alignment of technical solutions with business needs, facilitating feedback and iteration throughout the development process, and tailoring multi-modal capabilities to specific user requirements.
• Continuous Learning & Research : Stay abreast of the latest developments in AI/GenAI technologies,