APA Solution Engineer driving unprecedented growth with Agentic Process Automation. Leading innovative automation and AI - driven solution delivery in a dynamic team environment.
About the role
- 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.
Responsibilities
- 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
- 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
- Competitive salary
- Flexible working hours
- Professional development budget
- Home office setup allowance
- Global team events
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