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 / Qualifications
• 2-4 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/SQL
  • ML basics (train/test splits, overfitting, common metrics) and/or LLM application patterns
• Familiarity 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 Qualifications
• RAG (embeddings, vector databases, chunking strategies)
• Experience with GenAI application development patterns:
• Prompt engineering and prompt versioning
• Experience 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.