Junior AI Engineer

Title: Junior AI EngineerLocation: Charlotte, NCDuration: 6+ months (Long-term engagement)Position Summary: The Junior AI Engineer supports the design, development, and deployment of AI-enabled solutions that improve security operations and business workflows. This role focuses on building and iterating AI/ML and GenAI components (data prep, prompt/workflow design, evaluation, and lightweight model development), partnering with senior analysts, engineers, product owners, and operational teams to move prototypes into reliable services.Key Responsibilities (AI-Focused)Build AI to define success metrics, acceptance criteria, and guardrails for AI-enabled features.Required Skills / Qualifications2-4 years of experience in software engineering, data engineering, analytics, or applied ML (internships/academic projects welcome).Strong fundamentals in Python.Working knowledge of:Data structures, APIs, and basic software engineering practices (testing, code reviews, Git)Data handling with pandas/SQLML basics (train/test splits, overfitting, common metrics) and/or LLM application patternsFamiliarity with at least one AI/ML framework or platform (coursework/labs acceptable): PyTorch, TensorFlow, scikit-learn, or common LLM tooling.Ability to write clear documentation and communicate tradeoffs (quality vs cost vs latency).Preferred QualificationsRAG (embeddings, vector databases, chunking strategies)Experience with GenAI application development patterns:Prompt engineering and prompt versioningExperience with cloud services (AWS/Azure/GCP) and containerization (Docker/Kubernetes)Basic understanding of privacy/security fundamentals for AI systems (data handling, access controls, logging) Cybersecurity-aligned preferred experience (nice-to-have):Experience partnering with or supporting a SOC (e.g., translating analyst workflows into automations, alert triage enrichment, case summarization).Familiarity with SIEM/EDR concepts and data (e.g., Splunk/Sentinel-like searches, endpoint telemetry, detection event schemas) to build AI features on top of security telemetry.Exposure to threat intelligence & IOC handling (IPs/domains/URLs/hashes) and using AI to extract/normalize indicators from unstructured text.Working knowledge of incident response lifecycle and case management processes (ticketing, evidence handling, basic post-incident reporting).Awareness of secure software practices (secrets management, least privilege, dependency hygiene) when building and deploying AI services.

Created: 2026-05-13