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AI Engineer – Agentic Systems
Ample Insight. Solve real-world problems using structured and/or unstructured data .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in architecting and building production-grade AI/ML systems, with strong foundations in machine learning, deep learning, and statistics. Capable of transforming cutting-edge research into scalable solutions while ensuring observability and reliability in AI systems.
Highest-signal resume keywords
Production-Grade AI/ML SystemsMachine Learning Model DesignComplex Agentic Systems ArchitecturePython ProgrammingAI Evaluation and Observability
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningDeep LearningStatisticsOptimizationModel TrainingModel EvaluationData PrivacySecure AI ArchitecturesOrchestrationState Management
Soft Skills
Problem-SolvingProduct InstinctsBias Toward Action
Certifications & Qualifications
Degree in Computer ScienceDegree in Engineering
Industry Keywords
AI SystemsAgentic SystemsScalable InfrastructureObservabilityReliability
Tech Stack
Tools & technologiesPython
About the role
Key responsibilities & impact- Solve real-world problems using structured and/or unstructured data
- Productize scalable machine learning models
- Architect complex agentic AI systems with orchestration, tool use, memory, and state management
- Design, train, evaluate, and improve machine learning models
- Build production-grade AI/ML systems
- Work with AI evaluation, observability, reliability, and scalable infrastructure
- Turn cutting-edge research into working prototypes
- Work on client projects for startups and Fortune 500 companies
Requirements
What you’ll need- Degree in Computer Science or Engineering
- Strong Python skills
- Experience building production-grade AI/ML systems
- Experience architecting complex agentic systems, including orchestration, tool use, memory, and state management
- Strong theoretical and practical foundations in machine learning, deep learning, statistics, and optimization
- Experience designing, training, evaluating, and improving ML models
- Experience with AI evaluation, observability, reliability, and scalable infrastructure
- Ability to quickly turn cutting-edge research into working prototypes
- Strong problem-solving skills, product instincts, and a bias toward action
- Familiarity with data privacy, permission-aware AI systems, and secure AI architectures (bonus)
Benefits
Comp & perks- Competitive compensation
- Work on projects based on your interests and skills
- Accountability and not micro-management
- Remote work option