Solution Architect
7 days ago
Title : GenAI Solution Architect (Generative AI)
Experience : 8+ years
Work Location : Remote
Notice Period : Immediate to 30 Days Max only
Job Description :
- Client is looking for a Generative AI Solutions Architect who will be the Subject Matter Expert (SME) for helping various stakeholders in designing solutions that leverage our Generative AI services.
- You will interact with Business team directly to understand their problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths for generative AI.
- You will work closely with other Solution Architects from various geographies to enable large-scale customer use cases and drive the adoption of Azure/Databricks Services for GenAI services.
- You will interact with other Data Scientists and Solution Architects in the field, providing guidance on their customer engagements.
- You drive effective feedback gathering from customers, and you distill and translate that feedback into clear business and technical requirements for product and engineering teams to review.
- You must have deep technical experience working with technologies related to large language models including LLM architectures, model evaluation, and fine-tuning techniques.
- You must have experience with embedding model fine tuning and retrieval method evaluation approaches.
- You should be proficient with design, deployment, and evaluation of LLM-powered agents and tools and orchestration approaches.
- Design the network architecture to ensure optimal performance of generative AI applications.
- You should understand the security and compliance requirements for ML/GenAI implementations.
- You must have experience with LangChain, LLAMAIndex, Data Augmentation, Responsible AI, and Performance Evaluation frameworks. You should have experience architecting end to end ML/Gen AI applications for customers using Azure services and Well Architected Framework.
- Candidates must have great communication skills and be very technical, with the ability to impress Voya customers at any level, from executive to developer.
- You will get the opportunity to work directly with senior ML engineers and Data Scientists at customers, partners and Azure Web Services service teams, influencing their roadmaps and driving innovation.
Key job responsibilities :
- Customer Advisor- Implement, and deploy state of the art machine learning solutins under Gen AI. You will build prototypes, PoCs, and explore new solutions. You will interact closely with our stakeholders.
- Thought Leadership
- Evangelize Azure GenAI services and share best practices to the team and help them to deliver best-in class solution. Train other team members on the architecture and how to use it effectively.
- Participate in professional development activities to stay current with the latest trends and advancements in Azure and generative AI.
- Partner with Data Scientists, SAs, Sales, Business Development and the Generative AI Service teams to accelerate customer adoption and providing guidance on their customer engagements.
- Algorithm Selection : Choosing the appropriate generative AI algorithms and models based on the specific use case and desired outputs.
- Data Preparation : Overseeing the data preprocessing and preparation of training data to ensure that Gen AI models can learn meaningful patterns.
- Act as a technical liaison between business and the Generative AI services teams to provide customer driven product improvement feedback.
Basic Qualifications :
- 3+ years of experience in end to end technical architecture, design, deployment and operations for Generative AI/ML platforms and applications.
- 1+ year experience working with technologies related to large language models including LLM architectures, model evaluation, adapters, model customization including pre-training and fine-tuning techniques.
- Proficient with design, deployment, and evaluation of LLM-powered agents and tools and orchestration approaches.
- Proficient with prompt engineering, embedding model fine tuning and retrieval method evaluation and optimization approaches.
- 5+ years of experience in design/implementation for Machine Learning/AI/Deep Learning solutions
- Using one or more Deep Learning frameworks such as TensorFlow, PyTorch, etc.
- 5+ years professional experience in software development in languages related to ML like Python or Java.
- Experience working with RESTful API and general service-oriented architecture.
- 8+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience.
- Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, engineering, or computer science
Preferred Qualifications :
- Experience with optimizing ML workloads using Model compression, distillation, pruning, sparsification, quantization, Transformers based algorithms like FlashAttention, PagedAttention, Speculative decoding, Distributed training/inference optimization, Hardware-informed efficient model architecture.
- Experience with distributed training and optimizing performance versus costs.
- Experience with open source frameworks for building applications powered by language models like LangChain, LlamaIndex. Design, develop, and optimize high-quality prompts and templates that guide the behavior and responses of LLM.
- Experience with design, deployment, and evaluation of LLM-powered agents and tools and orchestration approaches
- Work Experience in deploying, versioning, monitoring, scalability, and performance optimization (LLMOps) of Generative AI models.
- Experience with Azure technologies such as ADF, Azure AI Search, Azure OpenAI, Document Intelligence Services,
- Demonstrated ability to think strategically about business, product, and technical challenges in an enterprise environment.
- Track record of thought leadership and innovation around Machine Learning.
- Experience with Performance benchmarking and developing prescriptive guidance on optimally building, deploying and monitoring ML models on Azure to drive actions at scale to provide low prices and increased selection for customers.
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