This position requires office presence of a minimum of 5 days per week and is only located in the location(s) posted. No relocation is offered.

The Insider Risk 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 engineers, product owners, and operational teams to move prototypes into reliable services.

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 andmaintaindata 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, basicMLOps(packaging, deployment, monitoring).

• Assistin 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.

Example Use Cases (Optional)

• Summarize tickets/incidents into standardized notes and action items.

• Auto-tag and route requests based on description content.

• Extract entities and indicators from unstructured text (emails, logs, reports).

• Build an internal knowledge assistant over approved documentation with citations.

• Generate draft playbooks/runbooks from templates and curated inputs.

Required Skills / Qualifications

• 02 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

• Experience with GenAI application development patterns:

• RAG (embeddings, vector databases, chunking strategies)

• Prompt engineering and prompt versioning

• Tool/function calling and agentic workflows

• Output evaluation and red-teaming basics (prompt injection awareness, safety filters)

• Exposure toMLOpsconcepts: CI/CD for ML, model registry, feature stores,monitoringdrift.

• Experience with cloud services (any of AWS/Azure/GCP) and containerization (Docker).

• Basic understanding of privacy/security fundamentals for AI systems (data handling, access controls, logging).

Preferred Qualifications (expanded to include cybersecurity-aligned experience)

• Experience with GenAI application development patterns:

• RAG (embeddings, vector databases, chunking strategies)

• Prompt engineering and prompt versioning

• Tool/function calling and agentic workflows

• Output evaluation and red-teaming basics (prompt injection awareness, safety filters)

• Exposure toMLOpsconcepts: CI/CD for ML, model registry, feature stores,monitoringdrift

• 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 aSOC(e.g., translating analyst workflows into automations, alert triage enrichment, case summarization).

• Familiarity withSIEM/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 tothreat intelligence & IOC handling(IPs/domains/URLs/hashes) and using AI to extract/normalize indicators from unstructured text.

• Working knowledge ofincident response lifecycleand case management processes (ticketing, evidence handling, basic post-incident reporting).

• Awareness ofsecure software practices(secretsmanagement, least privilege, dependency hygiene) when building and deploying AI services.

Education/Experience: Bachelors degree (BS/BA) desired in Computer Science or Cybersecurity. 5+ years of related experience. Certification is required in some areas.

Supervisor:

No

Joining our team comes with amazing perks and benefits:

• Medical/Dental/Vision coverage
• 401(k) plan
• Tuition reimbursement program
• Paid Time Off and Holidays (based on date of hire, at least 23 days of vacation each year and 9 company-designated holidays)
• Paid Parental Leave
• Paid Caregiver Leave
• Additional sick leave beyond what state and local law require may be available but is unprotected
• Adoption Reimbursement
• Disability Benefits (short term and long term)
• Life and Accidental Death Insurance
• Supplemental benefit programs: critical illness/accident hospital indemnity/group legal
• Employee Assistance Programs (EAP)
• Extensive employee wellness programs
• Employee discounts up to 50% off on eligible ATt mobility plans and accessories, ATt internet (and fiber where available) and ATt phone

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