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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in modernizing legacy systems into cloud-native architectures while implementing AI-driven solutions. Proficient in MLOps best practices, CI/CD, and integrating complex workflows with compliance in a regulated environment.
Highest-signal resume keywords
Cloud-Native ArchitectureAI/ML Application DevelopmentPython ProficiencyAWS Production ExperienceCI/CD and Infrastructure-as-Code
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Application ModernizationAgentic AI SolutionsMicroservices ArchitectureSQL ProficiencyRAG ImplementationVector SearchML/NLP LibrariesDockerKubernetesTerraform
Soft Skills
CollaborationProblem-SolvingCommunication
Tools & Technologies
LangChainLangGraphAWS BedrockHugging FaceTransformersPyTorchTensorFlowMLflowSageMakerCloudFormation
Certifications & Qualifications
Bachelor’s Degree in Computer ScienceU.S. Citizenship or Green Card
Industry Keywords
Regulated Federal Financial EnvironmentSecurity CompliancePrivacy GovernanceData IntegrationAI-Enabled Services
Tech Stack
Tools & technologiesAWSCloudDockerDynamoDBKubernetesMicroservicesPythonPyTorchScikit-LearnSQLTensorflowTerraform
About the role
Key responsibilities & impact- Modernize GMF and related legacy workloads by refactoring monoliths and batch processes into secure, cloud-native architectures with embedded AI/automation
- Design, build, and deploy LLM- and agentic AI-based solutions using technologies such as LangChain, LangGraph, RAG, vector search, and AWS Bedrock agents
- Automate complex workflows and integrate solutions with IRS data sources
- Implement platform engineering and MLOps/AIOps best practices, including CI/CD, infrastructure-as-code, model/prompt lifecycle management, and responsible AI controls
- Collaborate with architects, developers, testers, and stakeholders to design scalable, secure AI-driven modernization solutions
- Integrate legacy data sources into modern data platforms and AI-enabled services
- Ensure compliance with security, privacy, and governance requirements in a regulated federal financial environment
Requirements
What you’ll need- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field
- Ability to obtain and maintain a public trust requiring U.S. Citizenship or Green Card
- 9+ years in software, ML, or data engineering, including experience with application modernization
- 4+ years building and deploying AI/ML or LLM-based applications in production
- Strong experience with modern application architectures (microservices, REST APIs, event-driven) and legacy integration
- Hands-on experience building agentic AI solutions using LLM frameworks such as LangChain and LangGraph
- Proficiency in Python and common ML/NLP libraries such as Hugging Face, Transformers, scikit-learn, PyTorch/TensorFlow
- Production experience with AWS, including networking/IAM, Lambda, ECS/EKS, API Gateway, S3, DynamoDB, RDS, OpenSearch, SageMaker, and CloudWatch
- Practical experience using AWS Bedrock for LLM-powered applications and agents, including knowledge bases and guardrails
- Experience implementing RAG and working with vector search/databases
- Experience with CI/CD and infrastructure-as-code such as Terraform or CloudFormation
- Familiarity with MLOps/AIOps such as MLflow or SageMaker and AI-focused observability
- Strong SQL skills and experience integrating legacy data into modern platforms
- Experience with Docker and container orchestration such as Kubernetes or AWS ECS/EKS
- Desired: Experience with Databricks, LangSmith or similar tools, CrewAI, AutoGen, Temporal, Model Context Protocol, BrainTrust, DeepEval, or similar frameworks