Junior AI Engineer

Key Responsibilities (AI-Focused)Build AI prototypes and small services that solve defined problems (e.g., text classification, summarization, routing, search, Q&A, extraction).Develop and maintain data pipelines for AI use cases (collect, clean, label, transform) using approved data sources.Create and iterate on LLM prompts, agent/workflow logic, and retrieval-augmented generation (RAG) patterns for internal knowledge use cases.Implement evaluation methods for AI outputs (quality, groundedness, hallucination checks, latency, cost) and report results.Support model lifecycle tasks: experiment tracking, versioning, basic MLOps (packaging, deployment, monitoring).Assist in integrating AI components into existing applications via APIs and lightweight UI or automation.Document solutions (design notes, datasets, prompt versions, test cases) and contribute to internal reusable components.Collaborate with stakeholders 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.