AI Solutions Architect

Innovecs

AI Solutions Architect leading design and implementation of AI-powered solutions for Innovecs. Shaping AI architecture strategy and driving automation across product and engineering landscape.

Posted 5/19/2026full-timeRemote • 🌎 Anywhere in the WorldMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AWSAzureCloudERPGoGoogle Cloud PlatformITSMPythonTypeScript

About the role

Key responsibilities & impact

AI Products & Solution Architecture:
Design and guide implementation of AI-driven products, APIs, and platform features from concept to production;
Evaluate, select, and benchmark AI/ML models — including frontier LLMs, fine-tuned models, and open-source alternatives;
Architect scalable, observable, and cost-efficient AI systems that span experimentation, staging, and production;
Collaborate with product managers and business stakeholders to translate requirements into robust solution architectures;
Establish architectural standards for multi-agent systems, including context management strategies and memory designs.
Agentic AI & Process Automation:
Identify business processes that can be automated or enhanced via agentic AI, and define the architecture for doing so;
Design and oversee implementation of MCP server ecosystems that connect agents to enterprise data sources and tools;
Architect multi-agent workflows using orchestration frameworks (LangGraph, CrewAI, AutoGen), with appropriate human-in-the-loop checkpoints;
Integrate agent-to-agent communication standards (A2A, ACP) where multi-agent coordination is required;
Drive governance of MCP deployments: audit trails, authentication, rate limiting, and access control policies;
Embed AI into internal and external tools to improve operational efficiency across teams.
AI-Augmented Software Engineering:
Set up and continuously optimize AI-augmented developer environments (Claude Code, Cursor, GitHub Copilot);
Introduce AI into automated testing, deployment pipelines, code review, estimation, and technical documentation;
Define and enforce best practices for using AI coding tools safely, securely, and productively in software delivery;
Drive adoption of context engineering disciplines — designing prompts, tool schemas, and MCP resources that maximize agent reliability;
Governance, Security & Responsible AI:
Ensure all AI systems are designed with security-first principles: input validation, output guardrails, and least-privilege access;
Maintain AI compliance standards aligned with GDPR, the EU AI Act, EU Data Act, CRA, ISO27001, and internal model governance policies;
Implement observability and evaluation pipelines to detect hallucinations, drift, and performance degradation in production LLM systems.

Requirements

What you’ll need

Must-Have:
5+ years of experience in AI/ML solution architecture and demonstrable track record of taking AI systems from prototype to production at scale;
Deep expertise in LLMs, prompt and context engineering, RAG architectures, and vector databases;
Hands-on experience with agentic AI frameworks and orchestration;
LangChain / LangGraph: multi-step reasoning chains and stateful agent workflows;
LangWatch / CrewAI / AutoGen: multi-agent collaboration and task delegation;
MCP (Model Context Protocol): designing and deploying MCP servers for agent-to-tool integration;
Strong proficiency in Python; working knowledge of at least one additional language (Go, TypeScript, etc.);
Experience with cloud-native AI deployment on AWS, GCP, Azure, including managed LLM services such as Bedrock, Vertex AI, etc.;
Solid software engineering fundamentals: design patterns, API design, testing, and CI/CD;
Familiarity with AI observability, evaluation frameworks, and production monitoring of LLM-based systems;
Ability to define AI adoption roadmap, prioritize business cases based on ROI, present the strategic and tactical layers of implementation to both technical and business stakeholders;
Experience with AI compliance, governance frameworks, and explainability (GDPR, EU AI Act, model cards);
Experience integrating AI into enterprise systems (ERP, CRM, ITSM) via standardised protocols;
Experience leading engineering teams on AI-first projects.
Nice-to-Have:
Background in AI security: prompt injection mitigation, MCP server hardening, OAuth 2.x for agents;
Knowledge of emerging multi-agent communication standards: A2A (Google), ACP (IBM BeeAI);
Experience with reasoning/thinking models and their architectural implications for agent planning;
Contributions to open-source AI projects, MCP servers, or published technical articles.

Benefits

Comp & perks

Competitive salary
Flexible working hours
Professional development budget
Home office setup allowance
Global team events

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Hard Skills & Tools

AI/ML solution architectureLLMsprompt engineeringcontext engineeringRAG architecturesvector databasesPythoncloud-native AI deploymentCI/CDAI compliance

Soft Skills

collaborationcommunicationleadershipstrategic planningtactical implementationprioritizationstakeholder engagementproblem-solvinggovernanceprocess automation