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Provectus

ML Solutions Architect

Provectus Colombia 8 days ago
engineering
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 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

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