AI Engineer

Roboyo is a category shaper in Agentic Automation. We help leading brands embed autonomous, AI‑powered agents into their workflows, processes, products and services so they can scale faster and operate smarter.

Built on a strong automation heritage, we focus on seamless integration of AI into enterprise level organization, not just proving concepts, but owning outcomes and driving value in every industry we are present. At Roboyo, you’ll join a global team of builders, consultants and engineers that are top practitioners of taking solutions to the next level for clients in pursuit of excellence.

 

Job Description: AI Engineer - Agentic AI Solutions 

Position Overview 

We are seeking an experienced AI Engineer with deep expertise in developing and deploying agentic AI solutions on Microsoft Azure and/or Google Cloud Platform. This role focuses on designing, building, and operationalizing multi-agent systems that automate complex, end-to-end workflows. The ideal candidate will bridge the gap between AI prototypes and production-grade agentic systems, enabling enterprises to leverage autonomous intelligent agents for competitive advantage. 

Employment Type: Full-time 
Experience Level: 5+ years 
 

Technical Environment & Tools 

Primary Languages & Technologies: 

• Python (primary development language) 

• SQL 

AI/ML Frameworks & Libraries: 

• Semantic Kernel (Microsoft), Google Agent Development Kit (ADK), Vertex AI Agent Engine 

• LangGraph, LangChain, LLamaIndex 

• LLM platforms (OpenAI, Anthropic Claude, Google Gemini) 

Cloud Platforms: 

• Google Cloud Platform (Vertex AI, Cloud Run, BigQuery) 

• Microsoft Azure (AI Foundry, OpenAI, Cognitive Services, Container Services) 
• Power Automate and Copilot

Developer Tools: 

• Docker, Kubernetes, Git 

• FastAPI, Flask, or equivalent web frameworks 

• Jupyter Notebooks, VS Code 

• CI/CD platforms (GitHub Actions, Azure DevOps, Cloud Build) 

Observability & Monitoring: 

• cloud-native monitoring (Azure Monitor, Google Cloud Monitoring etc) 

• Logging frameworks and distributed tracing tools 

 

Key Responsibilities 

Agentic AI System Development 

• Design and develop scalable multi-agent systems from concept through production deployment using frameworks like Google ADK, Semantic Kernel, Langchain, LangGraph and AutoGen 

• Implement advanced agentic patterns including sequential, hierarchical, parallel, and hybrid agent orchestration strategies 

• Build sophisticated agent workflows with memory management (short-term working memory and long-term persistent memory), guardrails, and execution hooks 

• Develop and integrate custom tools, APIs, and Model Context Protocol (MCP) servers to extend agent capabilities and enable real-world task execution 

• Implement prompt engineering and instruction design for optimal agent reasoning, planning, and autonomy management 

Cloud Platform Integration & Deployment 

• Deploy agentic solutions on Microsoft Azure, including Azure AI Foundry, Azure OpenAI, and related AI services 

• Deploy and manage models on Google Cloud Platform using Vertex AI, Cloud Run, GKE 

• Design scalable infrastructure for multi-agent systems with proper load balancing, auto-scaling, and fault tolerance 

• Implement CI/CD pipelines and infrastructure-as-code (IaC) for reliable agent deployment and version control 

• Configure networking, security, access control, and compliance requirements for enterprise AI deployments 

System Reliability & Observability 

• Implement comprehensive logging, tracing, and monitoring using LangSmith,  OpenTelemetry or cloud-native observability tools, and custom metrics 

• Design evaluation frameworks using LLM-as-a-Judge techniques, scenario-based testing, and automated performance assessment 

• Build human-in-the-loop feedback mechanisms for continuous agent improvement and behavioural refinement 

• Establish guardrails, control layers, and safety mechanisms to prevent costly AI missteps and ensure alignment with business goals 

• Create production-grade monitoring dashboards and alerting systems for agent health and performance tracking 

Collaboration & Architecture 

• Collaborate with AI engineers, delivery leads, and business stakeholders to translate requirements into agent system designs 

• Design modular, reusable agent components and frameworks for accelerated development across projects 

• Participate in code reviews, architecture decisions, and technical documentation for agentic systems 

• Mentor junior engineers on agentic AI best practices, deployment strategies, and production considerations 

• Stay current with emerging trends, research papers, and industry best practices in agentic AI and autonomous systems 

 

Required Qualifications 

Core Experience 

• 5+ years of software engineering or AI development experience with demonstrated expertise in production-grade systems 

• 2+ years of hands-on experience with Large Language Models (LLMs), generative AI, and AI application development 

• Hands-on experience designing, building, or deploying enterprise-grade multi-agent systems or agentic AI solutions 

• Proven track record of deploying AI/ML systems to production on cloud platforms 

Technical Skills - Programming & Frameworks 

• Advanced Python proficiency: Strong command of Python for AI development, including async programming, dependency management, and performance optimization 

• Agentic AI Frameworks: Production experience with Microsoft Agent Framework, Semantic Kernel, Google Agent Development Kit, LangGraph, LangChain, or equivalent multi-agent orchestration frameworks 

• Tool Integration & APIs: Expertise in designing tool-calling interfaces, API orchestration, and webhook integration for agent workflows 

• Software Engineering: Proficiency in software design patterns, clean code practices, testing frameworks and version control (Git) 

Cloud Platform Expertise 

• Microsoft Azure: Production experience with Azure AI Foundry, Azure OpenAI, Azure Container Registry, Azure Kubernetes Service (AKS), and Azure Cognitive Services 

• Google Cloud Platform: Working knowledge of Vertex AI, Cloud Run, GKE, and BigQuery 

• Infrastructure & DevOps: Experience with containerization (Docker), orchestration (Kubernetes), CI/CD pipelines and cloud monitoring services 

Preferred Qualifications 

• Experience with advanced orchestration patterns for multi-agent systems (hierarchical, swarm-based, or consensus-driven) 

• Familiarity with observability tools such as LangSmith, Azure Application Insights, Google Cloud Trace, Arize or custom tracing solutions 

• Experience with vector store optimization, semantic search, and retrieval-augmented generation (RAG) patterns 

• Experience with model fine-tuning, prompt optimization, or custom model training 

• Familiarity with enterprise compliance requirements (SOC 2, HIPAA, GDPR) and audit trails 

• Experience with Microsoft Power Automate / Apps 

 

Education & Certifications 

• Bachelor's degree in Computer Science, Engineering, Data Science, or related field (or equivalent professional experience) 

• Azure or Google Cloud certifications (Azure AI Engineer Associate, Google Cloud Professional ML Engineer, or similar) are a plus 

• Relevant AI/ML certifications or completion of advanced agentic AI courses (DeepLearning.AI, Coursera) are advantageous 

 

Soft Skills & Attributes 

• Self-motivated learner: Ability to rapidly acquire knowledge in emerging AI technologies and adapt to new frameworks 

• Problem-solver: Comfortable with ambiguity; ability to break down complex problems and design pragmatic solutions 

• Collaborative: Works effectively with cross-functional teams including data scientists, product managers, and business stakeholders 

• Owner mentality: Takes end-to-end responsibility for system reliability from design through deployment and monitoring 

• Clear communicator: Ability to explain technical decisions and tradeoffs to both technical and non-technical audiences 

• Detail-oriented: Attention to code quality, testing coverage, and production readiness 

 

What We're Looking For 

Ideal candidates will demonstrate: 

1. Battle-tested production deployment experience with AI/ML systems at scale 

1. Deep hands-on expertise in building autonomous agent systems, not just theoretical knowledge 

1. Cloud platform fluency across both Azure and/or GCP with infrastructure design capabilities 

1. Engineering excellence with emphasis on reliability, observability, and maintainability 

1. Entrepreneurial mindset with ability to balance speed of development with production quality 

1. Passion for AI innovation while maintaining pragmatic focus on business outcomes 

 

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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