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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates advanced proficiency in Python software engineering and extensive experience in AI/ML engineering, particularly in architecting and delivering production-grade LLM applications. Capable of leading multidisciplinary teams through the full AI product lifecycle while ensuring high engineering quality and effective stakeholder communication.
Highest-signal resume keywords
AI/ML Engineering ExperienceProduction-Grade LLM Application DevelopmentAdvanced Python ProficiencyMLOps and AI CI/CD ExpertiseSystem Design Across Service Boundaries
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
Python Software EngineeringAI/ML EngineeringLLM Application DevelopmentMLOpsContainerizationOrchestrationAPI Design StandardsEmbedding ModelsNLP Similarity SystemsMulti-Agent Orchestration
Soft Skills
Clear CommunicationStrong OwnershipCollaborative Working StyleMentoringStakeholder Management
Tools & Technologies
DockerKubernetesAzure Container AppsAWS ECSClaude CodeCodexAI-Assisted Software Engineering ToolsObservability ToolsCloud DeploymentData Pipelines
Certifications & Qualifications
Bachelor's DegreeMaster's Degree
Industry Keywords
BankingCapital MarketsInsuranceAsset ManagementAI Platform StrategyModel Deployment ApproachesData SecurityAgile Delivery MethodologiesCost EfficiencyPerformance Improvement
Tech Stack
Tools & technologiesAWSAzureCloudDockerKubernetesPython
About the role
Key responsibilities & impact- Lead design, development, testing, deployment, and support for production-grade AI/ML, generative AI, and intelligent automation solutions
- Manage complex technical problems through coding, debugging, testing, troubleshooting, and structured design remediation
- Manage integration of LLM, RAG, and agentic solution components into enterprise applications and platforms
- Contribute to system design across service boundaries, orchestration layers, data flows, security controls, and external integrations
- Lead workstreams and project delivery through planning, coordination, execution oversight, issue management, and stakeholder communication
- Drive engineering quality through coding standards, CI/CD, automated testing, observability, and documentation
- Partner with Development, Engineering, Product, Data, Architecture, and engagement leadership teams
- Improve performance, resilience, maintainability, and cost efficiency of deployed AI systems
- Participate in architecture and design reviews, providing trade-off analysis and implementation guidance
- Use AI-assisted software engineering tools such as Claude Code, Codex, or equivalent platforms
- Advise clients on AI platform and infrastructure strategy, evaluation metrics, observability, security, and model deployment approaches
Requirements
What you’ll need- Bachelor's or master's degree
- Minimum of 6 years of applied engineering experience, including significant AI/ML engineering experience
- Ability to understand complex technical business challenges across banking, capital markets, insurance, and asset management and translate them into LLM-powered solutions
- Experience leading and managing multidisciplinary teams through the full AI product lifecycle
- Experience managing and mentoring AI engineers and data scientists
- Advanced hands-on Python software engineering proficiency
- Experience architecting and delivering production-grade LLM applications, including RAG, agentic orchestration, and structured output pipelines
- Knowledge of embedding models, vector search, semantic retrieval, and NLP similarity systems
- Expertise in LLM Ops, model lifecycle management, versioning, AI CI/CD, deployment governance, and production improvement loops
- Experience with multi-agent orchestration, tool use patterns, memory design, and human-in-the-loop workflows
- Experience governing agent behavior, audit trails, cost and latency controls, and reliability
- Experience defining and governing LLM evaluation frameworks
- Knowledge of MLOps, containerization, orchestration, and secure cloud deployment
- Experience governing API design standards for LLM and agentic systems
- Strong system design capability across service boundaries, asynchronous workflows, data contracts, cloud-native patterns, and secure deployment models
- Proficiency with Docker, Kubernetes, Azure Container Apps, AWS ECS, or equivalent technologies
- Ability to collaborate with data engineers, ML engineers, and business stakeholders
- Clear communication of complex AI system behavior and trade-offs to technical and non-technical stakeholders
- Strong ownership and accountability
- Collaborative, cross-functional working style
- Preferred familiarity with AI platform strategy, model observability, LLM fine-tuning, controlled model rollouts, LLM security risks, bias/fairness/explainability, data pipelines, cloud data security, GPU-accelerated workloads, and agile delivery methodologies
Benefits
Comp & perks- Medical and dental coverage
- Pension and 401(k) plans
- Flexible vacation policy
- EY Paid Holidays
- Winter/Summer breaks
- Personal/Family Care leave
- Other leaves of absence
- Professional growth opportunities
- Inclusive culture
- Performance-based compensation