Verterim, Inc.

Compliance

• Support multiple client assignments under the direction of senior managers and client stakeholders.
• Tailor, format, and maintain client policies using established templates (e.g., ISO/NIST).
• Drive annual review cycles: reminders, tracking, and sign‑off capture.
• Perform initial control mapping (e.g., policy → ISO 27001, NIST CSF/800‑53, CMMC) for senior review.
• Run and manage imports of GRC core data for example Employee / contactor tables, Asset tables, Exception tables.
• Coordinate vendor outreach, send questionnaires (e.g., SIG Lite, CAIQ), and follow up to closure.
• Execute monthly evidence hunts aligned to frameworks (ISO 27001, SOC 2).
• Produce weekly SLA reports (e.g., criticals > 14 days), open/route tickets, and track resolution.
• Prepare clean user lists for access reviews; highlight anomalies and terminations.
• Collaborate on agent design/tuning for: email drafting, evidence triage, tone/sentiment analysis, and process orchestration.

Junior GRC Analyst, Student Associate

AI Engineer

• Fine-tune and train large language models and other ML models as directed by Verterim's CTO, translating technical strategy into trained, deployable model artifacts.
• Design and execute fine-tuning pipelines (full fine-tuning, LoRA/QLoRA, instruction tuning, RLHF/DPO) tailored to GRC, risk, compliance, and audit use cases.
• Curate, clean, and structure training and evaluation datasets, including synthetic data generation and domain-specific labeling workflows.
• Optimize model performance across accuracy, latency, cost, and resource utilization for production deployment.
• Build and maintain rigorous evaluation harnesses, benchmarks, and regression suites to validate model quality prior to release.
• Ensure fine-tuned models meet enterprise expectations for accuracy, explainability, traceability, and defensibility in regulated environments.
• Detect and mitigate bias, hallucination, and drift through structured testing and continuous monitoring.
• Document model lineage, training data provenance, hyperparameters, and evaluation results to support audit and governance requirements.
• Manage end-to-end training infrastructure and workflows, including compute provisioning, experiment tracking, and versioning of models and datasets.
• Partner with AI Solutions Engineering to integrate trained models into RAG pipelines, agent orchestration frameworks, and knowledge-base systems.
• Establish reproducible, automated pipelines for retraining, fine-tuning refreshes, and model promotion from experimentation to production.
• Maintain technical documentation, training runbooks, and internal enablement artifacts for models in production.
• Serve as the primary technical point of contact for model training and fine-tuning matters between the CTO, engineering, and product teams.
• Ensure model development aligns with Verterim's internal AI governance model, quality funnel standards, and responsible AI principles.
• Partner with the CTO and Product Management to translate business, regulatory, and customer requirements into model training objectives.
• Support customer-facing technical discussions where deep model architecture or performance detail is required.

AI/ML Fine-Tuning and Training Engineer

Solutions Engineer

• Design and implement RAG (Retrieval-Augmented Generation) systems for document processing, policy analysis, and compliance automation
• Build and deploy AI applications using Azure OpenAI, Azure AI Foundry, and Azure Cognitive Services
• Develop multi-agent systems using frameworks like AutoGen, Semantic Kernel, or LangGraph
• Create production-grade REST APIs using Python (FastAPI) for AI services
• Implement prompt engineering strategies including few-shot learning, chain-of-thought, and structured output generation
• Integrate AI solutions with enterprise data sources (databases, document repositories, external APIs)
• Optimize LLM performance including context window management, token optimization, and response quality
• Handle LLM security including prompt injection prevention, guardrails, and content filtering
• Deploy and maintain AI infrastructure using Azure services and CI/CD pipelines (Bicep, Azure DevOps)
• Troubleshoot and resolve production issues in AI systems
• Collaborate with front-end teams (Next.js) to deliver complete solutions
• Collaborate with product managers, and business analysts to translate customer needs into actionable AI requirements.
• Contribute to building reusable frameworks, orchestration templates, and integration components for rapid AI solution deployment.
• Participate in code reviews, sprint planning, and Agile ceremonies to ensure consistent quality and high-velocity delivery.
• Assist in preparing technical documentation, deployment guides, and maintenance procedures.
• Work closely with senior engineers to integrate AI models into production applications, ensuring performance, reliability, and scalability.
• Stay current with Microsoft’s evolving AI ecosystem to identify opportunities for improvement and innovation.
• Propose enhancements to existing AI solutions through improved orchestration, automation, or model optimization.
• Participate in internal knowledge sessions to deepen expertise in LLM integration, prompt engineering, RAG implementations, and AI governance.
• Support experimentation with complex multi-agent coordination using AutoGen and Semantic Kernel.