Requirements

Expected technologies

TensorFlow

PyTorch

AWS

Our requirements

• Educational Background
• Bachelor's Degree or Master's Degree or Ph. D in Computer Science, Data Science, Artificial Intelligence, or a related field.
• Technical Skills
• AI & Machine Learning Expertise: Deep understanding of AI concepts, including machine learning algorithms, natural language processing, and deep learning frameworks (e.g., TensorFlow, PyTorch).
• System Architecture: Proficiency in designing scalable and robust AI systems, particularly for enterprise applications.
• Cloud Platforms: Extensive experience with AWS services, particularly in:
• Containerization (ECS, EKS, Fargate)
• Serverless computing (AWS Lambda, Step Functions)
• API management (API Gateway)
• Model Selection & Optimization: Ability to select appropriate AI models and frameworks based on performance metrics, cost-effectiveness, and specific use cases.
• Experience
• Industry Experience: 8+ years of experience in AI/ML roles, with a focus on architecture and system design.
• Project Management: Experience in leading AI projects and managing cross-functional teams.
• Multi-Agent Systems: Familiarity with designing and implementing multi-agent workflows and decision-making frameworks.
• Soft Skills
• Leadership Skills: Proven ability to lead teams and make high-level architectural decisions.
• Collaboration: Strong interpersonal skills to collaborate effectively with Data Scientists, ML Engineers, and Cloud Teams.
• Communication: Excellent verbal and written communication skills to articulate complex AI concepts to non-technical stakeholders.
• Governance & Compliance Knowledge
• AI Governance: Understanding of AI ethics, bias detection, model drift, and compliance with regulations.
• Security Awareness: Knowledge of security best practices in AI model deployment and data handling.
• Continuous Learning
• Staying Updated: Commitment to continuous learning in the rapidly evolving field of AI, including obtaining relevant certifications.
• Preferred:
• Relevant certifications in cloud architecture (e.g., AWS Certified Solutions Architect).
• AI/ML certifications from recognized institutions (e.g., Google Cloud Professional Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate).

Your responsibilities

• AI System Design & Strategy
• Architecting AI-powered systems for enterprise-level use cases.
• Designing scalable LLM architectures with:
• Document processing workflows
• Chatbot orchestration
• Vector RAG optimization
• Selecting the right AI models & frameworks based on use case, performance, and cost.
• Multi-Agent AI & Orchestration Strategy
• Designing multi-agent AI workflows that scale.
• Architecting rules-based & AI-based decision-making frameworks.
• Defining the best orchestrator functionality for multi-agent coordination.
• Cloud & Enterprise AI Infrastructure (AWS Focused)
• Architecting containerized AI services using AWS ECS, EKS, Fargate.
• Optimizing AI pipelines with AWS Lambda, Step Functions, API Gateway.
• Implementing secure and scalable AI governance models on AWS.
• AI Governance & Model Optimization
• Designing AI monitoring & governance frameworks (bias detection, model drift, compliance).
• Overseeing LLM evaluation, security & performance tuning.
• Managing AI integration with enterprise data pipelines.
• Leadership & Decision-Making
• Making high-level AI architectural decisions for model deployment.
• Collaborating with Data Scientists, ML Engineers, and Cloud Teams to ensure a scalable AI ecosystem.
• Defining AI best practices & reusable frameworks for engineering teams.