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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive expertise in designing and building scalable AI platform services and APIs, with a strong focus on Python, cloud-native technologies, and AI/ML solutions. Proficient in developing reusable components, frameworks, and automated CI/CD pipelines for enterprise applications.
Highest-signal resume keywords
Python Software EngineeringCloud-Native Application DevelopmentAI/ML Solution DeploymentAgentic AI FrameworksObservability and Reliability Engineering
ATS Keywords
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Hard Skills
API DevelopmentCI/CD PipelinesModel Lifecycle ManagementTesting and DebuggingContainerizationKubernetesFastAPIVector DatabasesSemantic SearchObservability Tools
Soft Skills
CollaborationProblem-SolvingCommunication
Tools & Technologies
Azure Machine LearningDatabricksLangGraphLangChainMLflowNeo4jGraphRAGRDF/SPARQLAutoGenAgent Tool Ecosystems
Industry Keywords
AI EngineeringData PlatformMulti-Agent SystemsGenerative AIEnterprise APIs
Tech Stack
Tools & technologiesAzureCloudIoTKubernetesNeo4jPython
About the role
Key responsibilities & impact- Design and build reusable enterprise platform capabilities for developing, deploying, and operating AI agents and multi-agent workflows
- Build frameworks and reusable components for secure agent interaction with enterprise APIs, data platforms, applications, tools, and knowledge sources
- Develop framework-agnostic orchestration patterns using agentic AI frameworks
- Build RAG and enterprise knowledge retrieval services, including document ingestion, retrieval pipelines, vector/semantic search, grounding, citations, and contextual enrichment
- Develop systematic approaches for agent and LLM evaluation covering task success, response quality, grounding, tool-use correctness, latency, reliability, and cost
- Implement observability and tracing for agent execution, model calls, tool calls, failures, performance, and resource consumption
- Design, build, and operate scalable AI platform services and APIs using Python and FastAPI or comparable frameworks
- Build reusable APIs, SDKs, libraries, templates, and reference implementations for AI application development and deployment
- Design cloud-native AI platform services using Azure, Kubernetes, Databricks, Azure Machine Learning, MLflow, or equivalent platforms
- Develop production capabilities for model and application deployment, configuration, versioning, experimentation, evaluation, and lifecycle management
- Build automated CI/CD and testing pipelines for AI services, agent workflows, prompts/configurations, and platform components
- Convert prototypes and notebook experimentation into modular, testable, maintainable production-quality software
- Design platform patterns for data quality, lineage, access control, and traceability across AI workflows
- Implement testing, security, scalability, reliability, maintainability, monitoring, alerting, guardrails, auditability, and human oversight
- Handle agent failures, tool failures, model errors, timeouts, retries, and degraded dependencies
- Optimize AI workloads for latency, throughput, infrastructure utilization, and model/token cost
- Diagnose complex production issues across AI models, orchestration, data, APIs, infrastructure, and applications
- Collaborate with data engineers, AI/ML engineers, application teams, architects, product teams, and domain experts to take AI capabilities from experimentation to secure, reliable enterprise services
Requirements
What you’ll need- 8+ years of software, data platform, ML platform, or AI engineering experience, including designing, building, and supporting production-grade systems
- Strong Python software engineering expertise, including API/service development, testing, debugging, packaging, design patterns, and modern software development best practices
- Experience building cloud-native distributed applications and platforms using containers, Kubernetes, public cloud technologies, CI/CD pipelines, observability, and reliability engineering
- Hands-on experience developing and deploying AI/ML and Generative AI solutions, including model serving, lifecycle management, monitoring, operational support, and production deployments
- Experience building LLM and agentic AI applications, including RAG/grounding, enterprise data integration and retrieval, orchestration frameworks, and tool/API integration
- Experience with LangGraph, LangChain, Semantic Kernel, AutoGen, or equivalent agent orchestration frameworks
- Experience designing multi-agent systems, agent tool ecosystems, planning/reasoning workflows, agent memory, or human-in-the-loop patterns
- Experience with AI evaluation and observability platforms such as LangSmith, MLflow, or equivalent tooling
- Experience with Azure Machine Learning, Azure AI services, Databricks, Kubernetes, or comparable AI/data platforms
- Experience with vector databases, semantic search, knowledge graphs, RDF/SPARQL, Neo4j, ontology engineering, or GraphRAG-style knowledge systems
- Experience building AI systems integrating with industrial IoT, operational technology, building systems, digital twins, or high-volume telemetry
- Experience building developer platforms, SDKs, internal frameworks, or reusable engineering capabilities used by multiple development teams
- Bachelor's or advanced degree in Computer Science, Engineering, Artificial Intelligence, or a related discipline, or equivalent practical experience
Benefits
Comp & perks- Employer-subsidized Medical, Dental, Vision, and Life Insurance
- Short-Term and Long-Term Disability
- 401(k) match
- Flexible Spending Accounts
- Health Savings Accounts
- Employee Assistance Program (EAP)
- Educational Assistance
- Parental Leave
- Paid Time Off for vacation, personal business, sick time, and parental leave
- 12 Paid Holidays
- Performance-driven salary
- Cutting-edge work and collaboration with dedicated experts