Services Architect

Ingram Micro touches 80% of the technology you use every day with our focus on Technology Solutions, Cloud, and Commerce and Lifecycle Solutions. With $50 billion in revenue, we have become the world’s largest technology distributor with operations in 64 countries and more than 35,000 associates.

**Experience**:

- ** Total IT Experience**: 8 to 15 years
- ** AI Experience**: 3 to 5 years
- ** Gen AI Experience**: 1 to 2 years

**Key Responsibilities**:

- ** Design, Architect and Develop AI Solutions**: Develop scalable and reliable AI solutions on AWS or any cloud platform.
- ** AI Services Expertise**: Proficient in AI services including EC2, Auto Scaling, S3, RDS, DynamoDB, Sagemaker, ML Studio, AI Studio, Vertex AI, IBM Watson
- ** AI Architecture Management**: Implement and manage AI architecture, tools, techniques, and frameworks.
- ** Data Handling and Analysis**: Expertise in RAG, Knowledge DB (Vector DB), Embedding, Indexing, Knowledge Graph, Cosine Similarity, and Searching.
- ** Gen AI Solutions Development**: Design, develop, train/tune/transfer learning, validate, deploy, manage/monitor, and optimize Gen AI solutions.
- ** Transformer Architecture**: Understanding of Transformer Architecture, including GAN, VAE, and VE.
- ** LLM Fine-Tuning and Prompt Engineering**: Expertise in fine-tuning large language models (LLMs) for specific tasks, using prompt engineering techniques such as few-shot learning and prompt chaining, and optimizing prompts.
- ** Multimodal AI Techniques**: Knowledge of multimodal AI techniques, including vision-language models (e.g., CLIP, DALL-E), speech-language models, and multimodal fusion techniques.
- ** Responsible AI Practices**: Understanding of responsible AI practices, techniques for detecting and mitigating bias in LLMs, and strategies for promoting fairness and transparency.
- ** LLM Evaluation Metrics**: Familiarity with evaluation metrics specific to LLMs, such as perplexity, BLEU score, and metrics for coherence, consistency, and factuality.
- ** Production Deployment of LLMs**: Experience in deploying LLMs in production environments, including containerization, scaling, and monitoring techniques for LLM models and pipelines. Understanding of Nvidia NIM, Open AI, Huggin Face etc.
- ** Security and Privacy**: Knowledge of security and privacy considerations when working with LLMs, such as data privacy, model extraction attacks, and techniques for secure model deployment.
- ** Continual Learning for LLMs**: Understanding of continual learning techniques for LLMs, such as rehearsal, replay, and parameter isolation, to enable efficient adaptation to new data and tasks.
- ** Model Creation and Evaluation**: Creating models using training and test datasets, evaluating models using algorithms, and performing hyperparameter tuning.
- ** Machine Learning**: Proficient in sci-kit learn for supervised and unsupervised ML, including NLP, recommender systems, anomaly detection, and time series.
- ** Custom AI Solutions**: Experience with custom object detection, speech recognition, image classification, and recognition.
- ** Deep Learning**: Implementing deep learning scenarios on NVIDIA or GPU-based hardware. Build, train and test SLM/LLM/Model
- ** LLM/Model Selection and Evaluation**:

- Select and evaluate LLMs based on use case requirements, cost, performance, and efficiency.
- ** Data Engineering and Management**:

- Experience with Python, SQL, and knowledge of Java.
- cleaning data, transforming data, preparing data
- ** DevOps/LLMOps Skills**:

- Implement configuration management and infrastructure as code (IAC) using CloudFormation and Elastic Beanstalk.
- ** Security and Compliance**:

- Ensure security and compliance of AI solutions on AWS using IAM, KMS, WAF, and other AWS security services.
- Implement VPC security best practices and maintain security compliance and governance on AWS.
- ** Development and Deployment**:

- Develop, train, and deploy machine learning models on AWS.
- Implement AI cognitive services, embedding/vector DB, and Gen AI enterprise integration (e.g., Open AI, Hugging Face).
- ** AI and Gen AI**:

- Expertise in machine learning and deep learning models, including Transformers and GPT.
- Experience with AI cognitive services and embedding/vector DB.
- Gen AI Enterprise Integration (Open AI, Hugging face, SAP, DFSC etc.)
- Gen AI Use Cases: Conversational, Summarization, Extraction, Document Processing, Task Automation, Enterprise Automation, RPA etc. in different Domains like Sales, Services, Customer Operations, Manufacturing, Logistics etc.
- Organization Change Management (OCM)
- Set-up AI CoE and AI Factory
- Processes, Templates, Formats, Org Structure
- Organization Change Management (OCM)
- ** LLMOps/DevOps**:

- CI/CD automation, configuration management, and cloud solutions deployment.
- Experience with AWS Lambda, API Gateway, ECS, and monitoring tools like CloudWatch.
- ** Data Engineering**:

- Design data pipelines,