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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 architecting AI-powered systems, integrating enterprise data and business processes, and developing production-ready software platforms. Proficient in leading technical discussions, mentoring engineers, and driving engineering best practices across teams.
Highest-signal resume keywords
AI System DesignProduction AI ApplicationsBackend Engineering FundamentalsMLOps PracticesProgramming Skills in Python
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AIMachine LearningSoftware EngineeringContext EngineeringAPIsCloud-Native ArchitecturesDevOpsMLOpsRapid PrototypingAI-Driven Architectures
Soft Skills
Strong Communication SkillsMentoringCollaborationProblem-SolvingAdaptability
Tools & Technologies
TerraformHelm ChartsCI/CDDistributed ServicesObservability Tools
Certifications & Qualifications
Bachelor’s Degree in Computer ScienceMaster’s Degree in AI or Related Field
Industry Keywords
Enterprise Data IntegrationContinuous ImprovementEvaluation and MonitoringAgile MethodologiesCloud Technologies
Tech Stack
Tools & technologiesCloudPythonTerraformTypeScriptGo
About the role
Key responsibilities & impact- Design and architect AI-powered systems using LLMs, RAG, agentic workflows, and orchestration patterns integrated with enterprise data and business processes
- Develop secure, maintainable, production-ready software platforms and cloud-native services
- Orchestrate models, tools, retrieval systems, and enterprise workflows
- Build rapid prototypes and proof-of-concepts to validate technologies and identify business opportunities
- Establish evaluation, monitoring, testing, benchmarking, observability, and continuous-improvement practices
- Lead technical design discussions and architecture reviews
- Drive engineering best practices across teams
- Mentor engineers and develop reusable AI capabilities and frameworks
- Collaborate with product teams, architects, domain experts, customers, and partners
- Influence IFS’s AI strategy and long-term technology direction through delivery, experimentation, customer engagement, industry events, and partner collaboration
Requirements
What you’ll need- Bachelor’s degree in computer science, Software Engineering, AI, Data Science, or a related field
- 8+ years of professional experience in AI, Machine Learning, and/or Software Engineering
- Proven track record of successfully delivered projects
- Demonstrable, hands-on experience designing, building and shipping production AI applications
- Experience bringing incubated AI solutions to production, including scoping, design, development, testing, deployment, and vigilant monitoring
- Strong programming skills in one or more mainstream programming languages such as Python, Golang, C# or TypeScript
- Experience with context engineering, including retrieval architecture, embeddings, vector databases, search technologies, and retrieval optimization techniques
- Strong backend engineering fundamentals, including APIs, distributed services, cloud-native architectures, CI/CD, integration, automation, and security
- Solid background in DevOps and MLOps/LLMOps practices
- Familiarity with infrastructure-as-code tools such as Terraform and package managers such as Helm Charts
- Ability to design solutions integrating enterprise applications, business processes, workflows, and data platforms
- Experience designing and implementing AI-driven architectures using LLMs, RAG, agentic workflows, orchestration patterns, and enterprise data sources
- Strong understanding of the AI system lifecycle, including evaluation, deployment, monitoring, governance, and continuous improvement
- Experience working closely with customers, stakeholders, and domain experts
- Demonstrated ability to rapidly prototype, experiment, measure outcomes, and iterate quickly
- Strong communication skills, with ability to explain complex technical concepts to technical and non-technical audiences
- Ability to operate in ambiguous, fast-moving environments and translate business problems into technical strategies and measurable outcomes
- Experience leading technical discussions, influencing architectural direction, mentoring engineers, and driving alignment across teams
- Experience with two or more of the listed AI, cloud, infrastructure, model development, or enterprise software technologies is highly desirable
- Master’s degree is advantageous
- Experience with MLOps lifecycles, deployment pipelines, model operations, and observability for AI systems is advantageous
Benefits
Comp & perks- Flexible and hybrid work opportunities
- Inclusive workplace experiences
- Opportunity to work in a global, diverse environment
- Commitment to sustainability
- Opportunity to contribute to worldwide-impact AI solutions
- Mentoring and collaboration opportunities
- Opportunity to participate in industry events and partner collaboration