Company

AI practice - Diego Martinez

AI/MLSolutionsArchitect

Medellín, Antioquia, Colombia FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“AI/ML Solutions Architect. Skills: ML Solutions, Client-Facing, Technical Leadership, Cloud Architecture. Lead technical discovery sessions. Understand client business problems”

What You'll Achieve.

Ensure solutions are feasible; Ensure solutions are scalable; Ensure solutions aligned with client needs; Ensure client satisfaction

Industry & Context.

AI practice Diego Martinez
Problems you'll solve

Solve business problems; Design ML solutions; Feasibility assessment

What They're Looking For.

Must Have

ML Architecture and Design, ML Lifecycle, System Design, Trade-off Analysis, Feasibility Assessment, ML Breadth, Multiple ML Domains, LLM Solutions, Classical ML, Deep Learning, MLOps, Cloud and Infrastructure, AWS Expertise, GCP Expertise, Multi-Cloud Awareness, Serverless Architectures, Cost Optimization, Security and Compliance, Data Architecture, Data Pipelines, Data Storage, Data Quality, Real-time vs Batch

What You'll Do.

Lead technical discovery sessions

Understand client business problems

Translate problems into ML solutions

Design end-to-end ML architectures

Create technical proposals

Present technical solutions

Estimate project scope

Support General Managers

Serve as technical point of contact

Manage technical stakeholder expectations

Present solutions to audiences

Navigate organizational dynamics

Ensure client satisfaction

Build trusted advisor relationships

Collaborate with delivery teams

Provide technical guidance

Contribute to reusable patterns

Share learnings and best practices

How You'll Work.

Team & Collaboration

Collaborate with delivery teams; Provide technical guidance; Contribute to reusable solution patterns; Share learnings and best practices; Mentor engineers

Communication Scope

Client communication; Technical presentations; Technical demonstrations; Present technical solutions; Communicate with stakeholders

Process & Methodology

Estimate project scope, Estimate timelines, Estimate cost, Estimate resource requirements

Full Job Description

## Description As an 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 ## 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 appr

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