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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and evaluating production-grade Agentic AI systems, leveraging advanced AI coding agents and frameworks to enhance software development workflows. Proficient in Python, API design, and multi-agent architectures, with a strong focus on scalability, reliability, and engineering best practices.
Highest-signal resume keywords
Agentic AI System DevelopmentPython DevelopmentLLM Application PatternsAgent Evaluation FrameworksMulti-Agent Architecture Design
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
Software EngineeringAI EngineeringProduction-Grade Application DevelopmentAgentic EvaluationAutomated Regression TestingAPI DesignScalabilityDebuggingTestingContext Management
Soft Skills
Technical LeadershipMentoringCollaborationProblem-SolvingCommunication
Tools & Technologies
Claude CodeLangGraphLangChainFastAPIDockerKubernetesAWSAzureGCPMCP
Industry Keywords
Agentic EngineeringAI Coding AgentsSoftware Development LifecycleCloud-Native ArchitecturesDistributed Systems
Tech Stack
Tools & technologiesAWSAzureCloudDistributed SystemsDockerGoogle Cloud PlatformKubernetesPythonSDLC
About the role
Key responsibilities & impact- Drive adoption of Agentic Engineering practices across the software development lifecycle using AI agents to augment and automate engineering workflows
- Leverage Claude Code, Claude Code Skills, PI, Hermes Agent, and comparable AI coding/engineering agents in day-to-day software development
- Build AI-assisted workflows for requirements analysis, code generation, code understanding, refactoring, testing, debugging, documentation, code review, and deployment
- Design agent workflows that understand large codebases, manage context, use tools, execute multi-step engineering tasks, and recover from failures
- Establish practices for context management, context engineering, tool calling, agent orchestration, guardrails, human-in-the-loop workflows, and autonomous task execution
- Design and implement evaluations measuring agent correctness, reliability, code quality, task completion, regression, and effectiveness
- Evaluate emerging agentic coding tools and techniques to improve engineering productivity and software quality
- Architect and develop production-grade multi-agent and agentic systems for complex multi-step tasks
- Design architectures involving planning, reasoning, tool use, memory/context, execution, reflection, validation, and error recovery
- Integrate agents with APIs, databases, enterprise systems, developer tools, and external services
- Develop reliable tool-use and MCP-based integrations
- Build production-grade LLM applications using LangGraph, LangChain, or equivalent orchestration frameworks
- Implement RAG, semantic search, vector retrieval, structured outputs, and other LLM application patterns
- Establish observability, evaluation, monitoring, security, and guardrails for agentic applications
- Provide technical leadership across AI-powered software products and platforms
- Apply system design, modular architecture, API design, scalability, reliability, testing, CI/CD, and maintainability principles
- Build production-quality services and APIs using Python, FastAPI, Docker, Kubernetes, and cloud platforms
- Collaborate with engineering, product, data, and client teams to translate business problems into scalable technical solutions
- Conduct technical design reviews and mentor AI/software engineers
- Establish engineering standards and best practices for AI and agentic applications
- Lead Agentic AI initiatives and influence architecture and engineering decisions
- Mentor engineers on AI engineering, agentic architectures, software engineering practices, and AI-assisted development
- Stay current with AI coding agents, agent frameworks, LLM capabilities, evaluation methodologies, and engineering practices
- Prototype emerging technologies and transition successful approaches into reliable production solutions
- Collaborate with clients and internal stakeholders to identify opportunities for measurable Agentic AI value
- Help Blend360 adopt Agentic Engineering across the SDLC, improve developer productivity, establish repeatable approaches, safely adopt AI coding agents, raise solution quality/reliability/scalability, and become a technical authority
Requirements
What you’ll need- 6+ years of software engineering / AI engineering experience
- Strong hands-on development experience
- Strong software engineering fundamentals and production-grade application/service development experience
- Demonstrable experience building production-grade Agentic AI systems beyond simple chatbots or basic RAG applications
- Strong understanding of Agentic Evaluation / Agent Evals
- Experience creating evaluation datasets, test scenarios, metrics, automated regression tests, and quality gates for agentic applications
- Ability to evaluate planning, tool use, reasoning trajectory, context handling, reliability, safety, latency, cost, and task completion
- Strong hands-on experience with Python and modern backend/API development
- Experience with LLMs, GenAI, agent orchestration, tool calling, and RAG
- Experience with agent frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Google ADK, or equivalent
- Strong understanding of multi-agent architectures, planning, reasoning, context management, tool use, memory, and agent execution
- Experience with evaluation frameworks to measure and improve AI/agent performance
- Experience with cloud, containers, CI/CD, APIs, databases, and production deployments
- Strong understanding of software architecture, debugging, testing, scalability, and production engineering practices
- Practical exposure to using AI agents as engineering tools within the SDLC
- Experience with Claude Code / Claude Code Skills, PI, Hermes Agent, AI coding agents, or comparable agentic development platforms
- Experience with MCP and building MCP servers/tools is nice to have
- Experience with Claude, GPT, Gemini, Llama, or other frontier models is nice to have
- Experience with AWS, Azure, or GCP is nice to have
- Experience with Kubernetes, Docker, CI/CD, and cloud-native architectures is nice to have
- Experience with LLM observability and tracing is nice to have
- Experience with Langfuse, Arize Phoenix, OpenTelemetry, or similar is nice to have
- Experience implementing automated agent evaluations, regression testing, and quality gates is nice to have
- Experience with distributed systems and scalable AI inference is nice to have
- Experience working in consulting/client-facing environments is nice to have
Benefits
Comp & perks- Remote work option
- Full-time employment