Core Responsibilities

Pre-Sales and Solution Design (45%)

• 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.
Client-Facing Technical Leadership (25%)
• 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.

Agentic Solutions Architecture (15%)

• Architect agentic AI solutions that leverage autonomous decision-making and tool orchestration;
• Design MCP (Model Context Protocol) integration strategies for client environments;
• Evaluate and recommend appropriate agent frameworks (LangGraph, Claude Agent SDK, etc.) for client use cases;
• Create POC demonstrations showcasing agentic capabilities using AI-assisted development tools
• Advise clients on build vs. buy decisions for agentic components;
• Develop reference architectures for common agentic patterns (RAG agents, multi-agent systems, tool-using agents);
• Assess AgentOps requirements including monitoring, evaluation, and cost optimization.

Internal Collaboration and Handoff (15%)

• Collaborate with delivery teams to ensure smooth handoff;
• Provide technical guidance during project execution;
• Contribute to the development of reusable solution patterns and agentic accelerators;
• Share learnings and best practices with ML practice;
• Mentor engineers on client communication and solution design;
• Contribute to Provectus AI toolkit documentation and solution templates.

Technical Requirements

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.

Data Architecture

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.

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.

Success Metrics (First 90 Days)

Days 1-30:

Shadow 3-5 pre-sales engagements;

Build relationships with General Managers and sales team;

Complete onboarding to Provectus solution catalog and AI toolkit;

Contribute to at least 1 technical proposal;

Demonstrate proficiency with Claude Code and AI-assisted development for POC creation.

Days 31-60:

Lead 2-3 technical discovery sessions independently;

Create compelling technical demonstrations including agentic AI capabilities;

Successfully hand off 1-2 projects to delivery teams;

Build rapport with key clients;

Develop at least one reusable agentic solution pattern or reference architecture.

Days 61-90:

Win at least 1 new client engagement through technical leadership;

Establish yourself as trusted technical voice for agentic AI solutions;

Contribute to at least 1 reusable solution asset or AI toolkit component;

Receive positive feedback from clients and internal stakeholders;

Successfully architect and propose at least one agentic solution to a client.

What We Offer

High-visibility role working with diverse clients;
Opportunity to shape solution offerings and practice direction;
Work with cutting-edge ML, LLM, and agentic AI technologies;
Global exposure across LATAM, Europe, and North America;
Career path toward Practice Leadership or Principal Architect;
Learning budget and conference attendance;
Remote-first with regular client travel opportunities;
Access to latest AI tools and subscriptions for professional development.

Application Process

1. Initial Screening: Resume + preliminary discussion;
2. Technical Assessment: System design + ML/agentic architecture discussion (2 hours);
3. Case Study: Design solution for realistic client scenario including agentic components (presentation);
4. Behavioral Interview: Client communication and stakeholder management;
5. Final Round: Meet with Practice Leader and General Managers;
6. Offer.