FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and developing cloud-native AI solutions, with a strong focus on architecture, technical feasibility, and production readiness. Proficient in mentoring engineering teams and translating complex business requirements into effective technical solutions.
Highest-signal resume keywords
Cloud-Native Application ArchitectureGenerative AI and LLM Application PatternsProduction Software EngineeringTechnical Feasibility AssessmentMentoring Engineering Teams
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Software EngineeringApplication DevelopmentData IntegrationPrototypingDebuggingTechnical ImplementationPerformance OptimizationArchitecting Distributed SystemsTesting and EvaluationRisk Management
Soft Skills
Strong Communication SkillsClient-Facing SkillsProblem-SolvingTechnical Guidance
Tools & Technologies
AzureAWSGCPKubernetesTerraformVector DatabasesLow-Code Automation PlatformsMLOpsDevOpsAI Platforms
Certifications & Qualifications
Master's Degree in a Technical FieldRelevant Cloud CertificationsArchitecture Certifications
Industry Keywords
AI SolutionsGenerative AIAgentic SystemsRAG SolutionsMicroservicesAPIsEnterprise Data PlatformsArchitecture Review BoardObservabilityAI Application Lifecycle Management
Tech Stack
Tools & technologiesAWSAzureCloudDistributed SystemsGoogle Cloud PlatformKubernetesMicroservicesTerraform
About the role
Key responsibilities & impact- Design, develop, and deliver innovative, production-ready AI solutions for clients
- Own solution architecture, technical feasibility, engineering quality, and production readiness
- Architect and develop generative AI applications, agentic systems, RAG solutions, and AI-enabled enterprise workflows
- Lead solution architecture for AI build and proof-of-concept engagements
- Perform application coding, data integration, prototyping, debugging, and technical implementation
- Design and implement cloud-native AI solutions using Azure, AWS, GCP, and relevant AI platforms
- Define architecture and integration patterns for LLMs, vector databases, APIs, microservices, enterprise data platforms, and client systems
- Guide solutions through Architecture Review Board processes
- Assess technical feasibility during client discovery, solution scoping, and sales support
- Establish engineering standards, reusable architecture patterns, technical documentation, and development best practices
- Design evaluation frameworks, monitoring, observability, guardrails, and performance optimization
- Collaborate with AI engineering, data engineering, MLOps, DevOps, cloud infrastructure, and security teams
- Evaluate custom development, managed AI services, and low-code or automation platforms
- Identify and address performance, scalability, security, reliability, and cost risks
- Mentor engineers through architecture reviews, code reviews, technical guidance, and hands-on problem-solving
- Evaluate emerging AI frameworks, cloud capabilities, and engineering tools
Requirements
What you’ll need- Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field
- Minimum of 6 years of experience in production software engineering, cloud engineering, AI/ML engineering, or a related technical discipline
- Strong hands-on software engineering experience with modern programming languages, application development, and production-grade solutions
- Experience architecting cloud-native applications and distributed systems using Azure, AWS, or GCP, including APIs, microservices, enterprise integrations, and data workflows
- Working knowledge of generative AI and LLM application patterns, including prompt engineering, RAG, embeddings, vector search, and agentic workflows
- Ability to assess technical feasibility and architectural tradeoffs considering scalability, security, performance, reliability, and cost
- Understanding of software and AI application lifecycle management, including testing, evaluation, production deployment, monitoring, and operational support
- Strong communication and client-facing skills
- Ability to translate business requirements into technical solutions and explain complex architectural decisions
- Ability to mentor engineering teams
- Demonstrated ownership of technical delivery, engineering quality, and risk management
- Ability to travel up to 80%
- Must verify identity and eligibility to work in the United States
- Crowe is not sponsoring work authorization
- Preferred: Master's degree in a technical field or relevant cloud or architecture certifications
- Preferred experience with production AI applications using LLMs or agent frameworks
- Preferred experience with LLM and agentic-system evaluation frameworks
- Preferred advanced cloud and AI infrastructure experience, including Kubernetes, Terraform, observability, and managed AI services
- Preferred experience with RAG solutions and vector databases such as Pinecone, Weaviate, or FAISS
- Preferred familiarity with AI-assisted development and low-code automation platforms such as Microsoft Power Platform, Cursor, Replit, Retool, or n8n
Benefits
Comp & perks- Real flexibility to balance work with life moments
- Unlimited PTO
- Flexible remote work policy
- Supportive environment prioritizing sustainable, long-term performance
- Professional development and knowledge-sharing
- Career Coach support for career goals and aspirations
- Comprehensive total rewards package
- Equal employment opportunities and inclusive culture
- Accommodation and applicant assistance available