Provectus

Provectus

Provectus provides consulting services in artificial intelligence and machine learning, assisting businesses in integrating AI solutions to meet their unique objectives and enhance their operational capabilities across various industries.

Professional Services
251-1K
Founded 2010

Description

  • Lead technical discovery sessions with prospective clients.
  • Translate client business problems into machine learning solutions.
  • Design end-to-end ML architectures and technical proposals.
  • Create technical presentations and demonstrations for clients.
  • Estimate project scope, timelines, cost, and resource requirements.
  • Support General Managers in winning new business.
  • Serve as the primary technical point of contact for clients.
  • Manage technical stakeholder expectations and navigate conflicting priorities.
  • Present solutions to both technical and non-technical audiences.
  • Collaborate with delivery teams to ensure smooth handoff and provide technical guidance during execution.
  • Contribute reusable solution patterns and share best practices with the ML practice.
  • Mentor engineers on client communication and solution design.

Requirements

  • Experience architecting end-to-end ML systems for diverse business problems.
  • Deep understanding of the full ML lifecycle from data to deployment.
  • Experience designing scalable, production-grade ML architectures.
  • Ability to evaluate technical trade-offs across cost, performance, and complexity.
  • Ability to quickly assess whether ML is an appropriate solution for a problem.
  • Experience across multiple ML domains such as RAG, computer vision, time series, and recommendation systems.
  • Strong experience architecting LLM-based applications.
  • Foundation in traditional machine learning algorithms and when to use them.
  • Understanding of neural network architectures and deep learning applications.
  • Knowledge of production ML infrastructure, MLOps, and DevOps practices.
  • Advanced knowledge of AWS ML and data services.
  • Advanced knowledge of GCP ML and data services.
  • Understanding of Azure and other cloud alternatives.
  • Experience with serverless architectures such as Lambda and API Gateway.
  • Ability to design cost-effective solutions.
  • Understanding of data security, privacy, and compliance.
  • Understanding of ETL/ELT patterns and data pipeline tools.
  • Knowledge of databases, data lakes, and warehouses.
  • Understanding of data validation and monitoring.
  • Ability to design for both real-time and batch processing needs.

Interested in this position?

Apply directly on the company website

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