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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and deploying production-ready GenAI solutions, with a strong focus on LLM integration, AI agent orchestration, and performance optimization. Proficient in Python programming and familiar with cloud platforms and MLOps practices to ensure reliable AI system performance.
Highest-signal resume keywords
GenAI Solution DesignLLM IntegrationAI Agent OrchestrationPython ProgrammingCloud Platform Familiarity
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
GenAI SolutionsRAG ArchitecturesAI Agent Behavior EngineeringAPI DevelopmentAutomated TestingPerformance OptimizationPrompt EngineeringObservabilityMulti-Agent SystemsFine-Tuning
Tools & Technologies
AzureAWSGCPLangGraphLangChainLlamaIndexSemantic KernelCI/CDLLMOpsMLOps
Industry Keywords
Enterprise ApplicationsCRM IntegrationERP IntegrationMCP IntegrationRegulated Industries
Tech Stack
Tools & technologiesAWSAzureCloudERPGoogle Cloud PlatformPython
About the role
Key responsibilities & impact- Design and deploy production-ready GenAI solutions, including RAG systems, intelligent assistants, and agentic workflows.
- Engineer AI agent behavior, including tool use, context management, memory, routing, failure handling, and human oversight.
- Integrate LLMs, APIs, enterprise applications, and data sources while balancing performance, latency, and cost.
- Build evaluation frameworks, automated tests, and quality controls to measure AI reliability and detect regressions.
- Monitor, troubleshoot, and continuously improve AI capabilities in production.
- Document reusable engineering patterns and enable client teams to maintain and extend deployed solutions.
- Collaborate with Software Engineers, Data Engineers, Architects, and Governance specialists.
Requirements
What you’ll need- Hands-on experience designing, developing, and deploying production-ready GenAI and LLM-based solutions.
- Proven experience with RAG architectures, AI agents, and multi-step intelligent workflows.
- Experience evaluating, monitoring, and optimizing AI systems for reliability, performance, and cost.
- Strong software engineering background, including API development, testing, and enterprise integrations.
- Strong Python programming and software development fundamentals.
- LLM integration, prompt engineering, embeddings, retrieval, and grounding techniques.
- AI agent orchestration, tool calling, context management, memory, and human-in-the-loop workflows.
- LLM evaluation, automated testing, observability, and production monitoring.
- Familiarity with cloud platforms (Azure, AWS, or GCP), CI/CD, and LLMOps/MLOps practices.
- Experience with LangGraph, LangChain, LlamaIndex, or Semantic Kernel is nice to have.
- Knowledge of multi-agent systems, multimodal AI, fine-tuning, or open-source models is nice to have.
- Experience integrating AI with CRM, ERP, MCP, or enterprise automation platforms is nice to have.
- Consulting experience or exposure to regulated industries is nice to have.
Benefits
Comp & perks- International and multicultural projects.
- Continuous learning and professional development.
- Career growth and internal mobility opportunities.
- A collaborative, innovative, and entrepreneurial culture.
- Local benefits and working arrangements will be confirmed before publication.