Description:
Job Description:
Strong and proven ability to identify and categorize use cases
suitable for Generative AI usage and implementation. This is a key ask.
• Responsible for reactive and
proactive proposals responses for solutions involving Generative AI
(specially LLMs), Conversational AI & cloud AIaaS.
• Provide leadership for the
transformation of customer requirements into visions, strategies, and
roadmaps to implement Design AI enabled solutions, Data Science services
platform at enterprise scale.
• Strong expertise in the Data & AI architecture for one of the Cloudpartners –Azure, AWS, GCP
• Develop and implement
applications incorporating Generative AI models such as GPT, Anthropic
Claude,, Google Palm, Meta Llama, focusing on enhancing developer and
business productivity.
• Knowledge of MLOPS will be beneficial.
• Architect solutions
incorporating Retrieval Augmented Generation, In-context Memory,
Transformer Architectures, and Hierarchical Models, ensuring optimal
model performance and scalability.
• Apply middleware frameworks
such as LangChain and Llama Index for efficient indexing, retrieval, and
chaining of language models, enhancing the contextual understanding and
response generation of applications.
• Integrate multiple
components such as data processing, machine learning models, and
feedback mechanisms to address architectural challenges and ensure
flawless deployment.
• Keep abreast of emerging
trends, sophisticated patterns, dependencies in data, and advancements
in AI architecture, supplying to the refinement and innovation of
application development processes.
• Explore and implement
inference techniques in generative AI for making predictions or
generating new data based on observed input.
Qualifications:
Hands-on programming skill on at least one language node.js/Java/Python
Strong hands-on capabilities on “Artificial Intelligence” and “Machine Learning” PaaS components such as:
• Contextual Conversation design– for personalized and humanized interaction with end user for complex business cases
• Microsoft BOT service, Google DialogFlow EX, Amazon Lex
• NLP model - design, training and publishing for multiple languages
• Project experience and/or
skills Certification with generative AI including: Azure Open AI (GPT
3.5/4) , Google PaLM 2 and AWS Bedrock
• Custom Speech model -
Speech-to-text and Voice synthesis calibrated for language, accent,
pitch, tone, noise and business vocabs.
Standard Architectural Practices As Below:
• Omni-Channel Integration for AI. MLOPS knowledge.
• Deployment and publish for AI and ML services with ACR, ACI, Docker, Azure Kubernetes
• Azure/ AWS/ GCP
certifications, AWS Machine Learning Specialty, Google Certified Cloud
Engineer and Deeplearning.ai certifications on LLMs, prompt engineering
• Web app and services – Micro services, Azure functions, Logic apps, API management
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