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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Solutions Architect - **Company:** PROVECTUS INC - **Location:** United States - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Artificial Neural Networks, Computer Vision, Microsoft Azure, Cloud Computing, Databases, Data as a Services, Data Architecture, Data Validation, Extract Transform Load (ETL), Data Security, DevOps, Machine Learning, Data Processing, Data Storage Technologies, Large Language Models, Deep Learning, Multi-Cloud, Data Lakes, Machine Learning Operations, Api Gateway, Data Pipelines, Serverless Computing - **Published:** June 2, 2026 - **Apply:** https://careers.provectus.com/vacancy/ai-ml-solutions-architect-e92413ae/ ## About the Role Requirements: 1. 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 2. 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 * Classical ML: Foundation in traditional ML algorithms and when to use them * Deep Learning: Understanding of neural network architectures and applications * MLOps: Knowledge of production ML infrastructure and DevOps practices 3. Cloud and Infrastructure * AWS Expertise: Advanced knowledge of AWS ML and data services * GCP Expertise: Advanced knowledge of GCP ML and data services * Multi-Cloud Awareness: Understanding of Azure, GCP alternatives * Serverless Architectures: Experience with Lambda, API Gateway, etc. * Cost Optimization: Ability to design cost-effective solutions * Security and Compliance: Understanding of data security, privacy, and compliance 4. Data Architecture * Data Pipelines: Understanding of ETL/ELT patterns and tools * Data Storage: Knowledge of databases, data lakes, and warehouses * Data Quality: Understanding of data validation and monitoring * Real-time vs Batch: Ability to design for different data processing needs ## Description As an AI/ML Solutions Architect, you'll be the technical bridge between clients and delivery teams. You'll lead pre-sales technical discussions, design ML architectures that solve business problems, and ensure solutions are feasible, scalable, and aligned with client needs. This is a highly client-facing role requiring both deep technical expertise and strong communication skills. Core Responsibilities: 1. Pre-Sales and Solution Design (50%): * Lead technical discovery sessions with prospective clients * Understand client business problems and translate them into ML solutions * Design end-to-end ML architectures and technical proposals * Create compelling technical presentations and demonstrations * Estimate project scope, timelines, cost, and resource requirements * Support General Managers in winning new business 2. Client-Facing Technical Leadership (30%): * Serve as the primary technical point of contact for clients * Manage technical stakeholder expectations * Present technical solutions to both technical and non-technical audiences * Navigate complex organizational dynamics and conflicting priorities * Ensure client satisfaction throughout the project lifecycle * Build long-term trusted advisor relationships 3. Internal Collaboration and Handoff (20%): * Collaborate with delivery teams to ensure smooth handoff * Provide technical guidance during project execution * Contribute to the development of reusable solution patterns * Share learnings and best practices with ML practice * Mentor engineers on client communication and solution design ## Related Videos - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Reference Architecture of AI in the Cloud](https://www.wearedevelopers.com/videos/1613-reference-architecture-of-ai-in-the-cloud) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)