ML Solutions Architect

ML Architecture and Design

Solution Design: Ability to architect end-to-end ML systems for diverse business problems;

ML Lifecycle: Deep understanding of the full ML lifecycle from data to deployment;

System Design: Experience designing scalable, production-grade ML architectures;

Trade-off Analysis: Ability to evaluate technical approaches (cost, performance, complexity);

Feasibility Assessment: Quickly assess if ML is an appropriate solution for a problem.

Agentic Engineering & AI-Assisted Development

Agentic Architecture: Deep understanding of agent design patterns, state management, and orchestration frameworks;

Claude Ecosystem: Hands-on experience with Claude Code, Claude Agent SDK, and Anthropic's tool ecosystem;

MCP Proficiency: Understanding of Model Context Protocol architecture for designing client integrations;

Agent Frameworks: Practical knowledge of LangGraph, LangChain agents, and multi-agent orchestration patterns;

AI-Assisted Workflows: Demonstrated experience with AI coding assistants (Cursor, GitHub Copilot, Claude Code) for rapid prototyping;

Tool Ecosystem Design: Ability to architect function calling and tool use strategies for complex client requirements;

AgentOps Understanding: Knowledge of agent monitoring, evaluation frameworks, and cost optimization strategies;

POC Development: Ability to rapidly build compelling agentic demonstrations using AI-assisted development.

ML Breadth

Multiple ML Domains: Experience across various ML applications (RAG, Computer Vision, Time Series, Recommendation, etc.);

LLM Solutions: Strong experience in architecting LLM-based applications including agentic systems;

Classical ML: Foundation in traditional ML algorithms and when to use them;

Deep Learning: Understanding of neural network architectures and applications;

MLOps/LLMOps/AgentOps: Knowledge of production ML infrastructure and DevOps practices for all ML paradigms.

Cloud and Infrastructure (AWS Required)

AWS Expertise: Advanced knowledge of AWS ML and data services (SageMaker, Bedrock, Lambda, ECS, etc.);

Amazon Bedrock: Deep understanding of Bedrock agents, knowledge bases, and model hosting options;

Multi-Cloud Awareness: Understanding of Azure, GCP alternatives for comparative discussions;

Serverless Architectures: Experience with Lambda, API Gateway, Step Functions for agentic workflows;

Cost Optimization: Ability to design cost-effective solutions with clear TCO analysis;

Security and Compliance: Understanding of data security, privacy, and compliance requirements.

Nice-to-Have Technical Skills

AWS Certifications (Solutions Architect Professional, ML Specialty);

Experience with specific industries (Finance, Healthcare, Retail, etc.);

Knowledge of AI ethics and responsible AI practices;

Experience with edge ML and IoT deployments;

Published thought leadership (blogs, talks, whitepapers);

Contributions to open-source agent frameworks or MCP servers.

Data Pipelines: Understanding of ETL/ELT patterns and tools;

Data Storage: Knowledge of databases, data lakes, vector databases, and warehouses;

Data Quality: Understanding of data validation and monitoring;

Real-time vs Batch: Ability to design for different data processing needs.