• Own the end-to-end operationalization of ML and AI solutions—from development to scalable, reliable production systemsthat integrate seamlessly with other digital tools. • Design, automate, and maintain CI/CD pipelines for model training, testing, deployment, and retraining (Azure DevOps, Databricks). • Build, optimize, and version model lifecycle workflows, ensuring reproducibility, lineage, and governance across the ML/AI platform. • Monitor production models for performance, drift, reliability, and resource usage; implement automated retraining workflows. • Optimize compute, storage, and orchestration across the Databricks platform to ensure efficient, cost-effective operations. • Collaborate closely with ML/AI Scientists, Data Engineers, and DWH team to transform research-grade models into production-ready services. • Contribute to advancing our ML/AI platform, tooling, automation standards, and best practices.

• Design & Architecture: Maintain the monitoring architecture for AI/ML platforms and configure advanced dashboards in Grafana and Azure Monitor. • Environment Governance: Manage Azure Machine Learning (AML) workspace configurations, compute targets, and Databricks cluster lifecycles (including runtime versions and platform patching). • Resource Optimization: Oversee GPU resource allocation, reserved capacity, and cost-performance optimization to align with FinOps goals. • Security Integration: Ensure all AI services utilize private endpoints, VNET integration, and RBAC controls to protect sensitive citizen data. • Pipeline Engineering: Own the design, optimization, and remediation of Azure Data Factory (ADF) and Synapse pipelines. • Advanced Troubleshooting: Resolve complex bottlenecks related to authentication failures, data format changes, and ETL performance. • SOP Leadership: Author step-by-step Standard Operating Procedures (SOPs) for the L1 NOC team to handle routine monitoring and first-line triage. • Automation: Implement CI/CD pipelines for model training, testing, and deployment to AML endpoints. • Model Reliability: Configure data drift detection thresholds and automated retraining triggers. • Recovery Operations: Develop self-healing scripts and automated recovery runbooks for critical AI workflows. • Audit Management: Implement and maintain audit logging for all AI decisions and model outputs, ensuring logs flow to the SIEM/vSOC. • Regulatory Alignment: Conduct quarterly AI governance reviews to ensure compliance with NESA standards and data privacy guidelines.

Role Description Audubon is currently seeking an RBI Specialist to join our team. The RBI Specialist will be responsible for creating and maintaining RBI data within various IDMS software programs supporting client timelines, quality of data and client interactions post-report delivery. These responsibilities will be carried out under the supervision of the assigned integrity engineer and will work as a direct report to the Quality Manager. While not performing data analysis, the candidate will support improvement of, development of, and maintenance of, RBI procedures and maintaining documentation in accordance with company and client standards. This position requires strong organizational and communication skills and will support integrity engineering by ensuring all needs are met safely and efficiently. Must be proficient in IDMS software such as Antea, PCMS, Aware, Visions etc. with the ability to be cross trained on multiple. This position is remote but some periodic travel to company offices or client facilities may be required. This position will be a temporary position and will be reviewed after 3 months for Quality, Performance and workload. After reviewing process is complete, the role may be eligible for full time or part time status. Qualifications - Requires high school diploma or equivalent. - 10 years of experience in Asset Integrity, inspection, and NDT related functions. - Knowledge of Microsoft Office Suite. - Basic knowledge of NDT techniques (UT, MT, PT, VT) and associated application. Requirements - Follow and adhere to all Audubon and Client safety rules and regulations. - Adhere to Audubon Core Values. - Must be able to interpret and apply all applicable procedures associated with quality procedures per client and Audubon requests. - Review all data/reports completed on assigned inspection projects/jobs for accuracy and validity. - Track all assigned projects/jobs to ensure that all data is completed and meets specified delivery timelines. - Ensure that all data was completed per Audubon or client specific processes/procedures. - Maintain all record retention for inspection projects/jobs per Audubon guidelines. - Communicate with integrity engineers. - Communicate with clients for post-job requests as required. - Must be able to complete computerized reports, research procedural requirements, file paperwork, and fill out required reports. - Assist in quality control functions, as assigned. - Ability to generate and export reports from IDMS software. - Maintain calibration certificates, service records, and asset tracking documents. - Participate in audits, both internal and external, concerning inspection data, inspection programs, or internal procedures. - Support special projects and other operational needs as assigned. - Must possess a valid driver’s license. - Proven ability to prioritize and complete work assignments by specific deadlines. - Proven ability to communicate with customers, supervision, and co-workers. - Proven ability to follow precise directions and procedures as directed by supervision. - Proven ability to manage multiple priorities in a fast-paced environment. Company Description Equal Opportunity Employer/Veterans/Disabled

Caylent is a cloud native services company that helps organizations bring the best out of their people and technology using Amazon Web Services (AWS). We provide a full-range of AWS services including workload migrations and modernization, cloud native application development, DevOps, data engineering, security and compliance, and everything in between. At Caylent, our people always come first. We are a global company and operate fully remote with employees in Canada, the United States, and Latin America. We celebrate the culture of each of our team members and foster a community of technological curiosity. Come talk to us to learn more about what it means to be a Caylien! The Opportunity This is a senior technical client leadership role that blends deep hands-on ML expertise with strategic advisory and consulting skills. You will be the most experienced ML voice across a diverse and expanding book of customer engagements — from early-stage companies bringing ambitious ML ideas to market, to established enterprises modernizing how they build and operate AI systems on AWS. You will shape strategy, influence architecture, and leave every team you touch better than you found it. You bring the scientific depth to design and evaluate models rigorously, the engineering depth to architect production ML systems at scale, and the consulting instincts to translate both into business value for customers. If you have led the hard conversations, shaped the architecture decisions that mattered, and built the things others benchmark against — and you are looking to do that across a growing portfolio of varied and interesting customers — this is the role for you. What You'll Do - Lead end-to-end ML assessments across infrastructure, data pipelines, model lifecycle, and organizational readiness — producing recommendations that drive executive decision-making and earn Caylent the next engagement. - Partner with sales and solutions teams through the proposal and scoping phase, contributing the technical depth needed to shape well-grounded statements of work. - Serve as the senior technical authority on client engagements — possibly across multiple projects simultaneously — providing architectural guidance, ensuring technical quality from your project team members, and getting hands-on when the engagement demands it, without owning day-to-day implementation responsibilities. - Own or orchestrate high-quality POCs that give customers confidence before committing to a larger initiative. - Advise customers on ML operations standards and architecture — covering MLOps pipeline design, model lifecycle management, LLMOps patterns, and production monitoring frameworks — translating operational complexity into decisions and guardrails their teams can own and sustain. - Shape how Caylent wins its most technically complex opportunities — contributing the architectural thinking and credibility that turns prospects into customers. - Strengthen the ML practice from the inside — through peer guidance, technical interviews, and contributions to accelerators, reference architectures, and thought leadership content. What You Bring The non-negotiables - 10+ years in machine learning or AI, with a proven track record of leading client-facing engagements in a consulting or advisory capacity. - Deep, current knowledge of the AWS ML and GenAI ecosystem, with the ability to make and defend architectural decisions across the full ML lifecycle — from data and feature engineering through training, deployment, and monitoring. - Deep expertise in at least two or three ML domains — whether traditional ML, computer vision, NLP, time series, or others — combined with the judgment to assess, architect, and advise across the broader ML landscape. - Proven ability to architect and govern production ML systems end-to-end, translating MLOps, LLMOps, and broader AI operations complexity into standards and decisions that engineering teams can execute and executives can act on. - Deep expertise across foundation model adaptation — fine-tuning (LoRA, QLoRA, PEFT), alignment (RLHF, DPO), inference optimization (quantization, vLLM), and distributed training (DeepSpeed, FSDP) — combined with RAG and agentic system design, including multi-agent architectures, event-driven workflows, MCP integration, and human-in-the-loop patterns on AWS. Technical authority to prescribe the right approach and set architectural standards that teams can execute against. - Proven ability to operate independently in complex customer environments — navigating ambiguity, aligning stakeholders, and translating ML tradeoffs into business risk and value for both technical and executive audiences. Strong differentiators - AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional. - Experience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagements. - Exposure to varied industries and problem types in a consulting or client-facing context. - Deep fluency in responsible AI practices — model evaluation, bias detection, fairness frameworks, and AI governance — applied in enterprise deployments. - Hands-on experience designing and deploying SRE agents and AI-driven operations workflows in production — spanning automated incident detection, triage, and remediation — with the ability to integrate across observability platforms and translate AI operations outcomes into measurable business value. Technical Stack Our practice spans a broad range of ML domains. Candidates are expected to prescribe — not just recognize — with the judgment to maximize what AWS makes possible and the experience to know how open-source tooling strengthens it. ML Domains: Classical ML, Computer Vision, NLP, Generative AI & LLMs, AI Agents & Autonomous Systems, Intelligent Document Processing, Video Understanding, Speech & Audio, Time Series & Forecasting, Recommender Systems, Graph ML, Reinforcement Learning, Multimodal AI AWS ML Platform: SageMaker, SageMaker Pipelines, SageMaker Feature Store, SageMaker Model Registry, SageMaker Clarify, Bedrock (Agents, Knowledge Bases, Guardrails, AgentCore, Model Evaluation) Multi-provider LLM: Bedrock, Anthropic API, OpenAI API, Google Gemini API, Azure OpenAI — with the judgment to reason across provider tradeoffs in enterprise contexts AWS AI Services: Rekognition, Comprehend, Transcribe, Textract, Translate, Personalize, Neptune, Kinesis Video Streams, Polly Data Platform: Apache Spark / PySpark, Apache Kafka, Amazon Kinesis, Apache Iceberg, Delta Lake, Apache Hudi, AWS Glue Vector Databases: Pinecone, pgvector, Amazon OpenSearch (vector), Weaviate Frameworks: PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, HuggingFace (Transformers, PEFT, TRL), LangChain, LlamaIndex, DSPy, Ollama MLOps & Governance: MLflow, W&B, Airflow / MWAA (data orchestration), Dagster (asset-based pipelines), Kubeflow Pipelines, CI/CD, IaC (CloudFormation, CDK, Terraform), Docker, Kubernetes, ML Governance (lineage, data contracts, audit), Responsible AI / Bias & Fairness LLM Evaluation & Safety: RAGAS, LLM-as-judge patterns, DeepEval, NeMo Guardrails, Constitutional AI patterns, structured output validation Inference & Optimization: Triton, vLLM, SGLang, Trainium, Inferentia, Quantization (GPTQ, AWQ, bitsandbytes), SageMaker Neo Benefits - Pay in USD - 100% remote work - Generous holidays and flexible PTO - Competitive phantom equity - Paid for exams and certifications - Peer bonus awards - State of the art laptop and tools - Equipment & Office Stipend - Individual professional development plan - Annual stipend for Learning and Development - Work with an amazing worldwide team and in an incredible corporate culture NOTE: We’re unable to provide visa sponsorship now or at any time in the future. At Caylent, we are committed to fair, transparent, and inclusive hiring practices. As part of our recruitment process, we may use artificial intelligence (AI) tools or automated systems to assist with the screening and evaluation of applications to help match candidate qualifications with job requirements. These tools are designed to support — not replace — human decision-making. Final hiring decisions are always made by our trained recruitment professionals. If an AI or automated tool is used during your application process, it will only be in accordance with applicable laws and regulations, and your information will be handled in a secure and confidential manner. If you have any questions, please contact talent@caylent.com Caylent is a place where everyone belongs. We celebrate diversity and are committed to creating an inclusive environment for all employees. Our approach helps us to build a winning team that represents a variety of backgrounds, perspectives, and abilities. So, regardless of how your diversity expresses itself, you can find a home here at Caylent. We are proud to be an equal opportunity employer. We prohibit discrimination and harassment of any kind based on race, color, religion, national origin, sex (including pregnancy), sexual orientation, gender identity, gender expression, age, veteran status, genetic information, disability, or other applicable legally protected characteristics. If you would like to request an accommodation due to a disability, please contact us at hr@caylent.com.