• Build production software with code and Supernal's proprietary platform, including backend services, data models, and CRUD applications • Build and maintain integrations with external systems (APIs, webhooks, third-party tools, and data sources) that AI Employees can safely act on • Design, implement, and deploy conversational agents, including multi-turn flows, state management, and tool usage • Own end-to-end technical delivery for high-priority customer implementations, from architecture through production launch • Translate customer requirements and SOWs into clear technical designs, execution plans, and deliverables • Make and own architectural decisions across application design, API integrations, LLM orchestration, RAG design, and workflow decomposition • Handle real-world voice system challenges including latency, interruptions, fallbacks, error handling, and failure recovery • Write automated tests — unit tests for isolated logic and end-to-end tests for full system and user journey validation • Apply solid error handling: distinguish retryable vs. fatal failures, surface meaningful error messages, and avoid silent failures • Actively debug complex production issues across agent logic, prompts, integrations, and external dependencies • Partner with delivery and product leadership to manage timelines, scope, and technical tradeoffs during implementation • Review technical work for quality, scalability, and maintainability, setting a high bar for engineering excellence • Define, document, and evolve best practices for building and delivering reliable AI Employees

- Projetar, desenvolver e manter APIs e microsserviços backend em Python (FastAPI/Flask); - Construir e evoluir pipelines de IA: integração com LLMs (Azure OpenAI, OpenAI, Anthropic, Gemini), embeddings, vector search e orquestração de agentes; - Implementar e otimizar soluções de RAG (Retrieval-Augmented Generation) com bases vetoriais (Azure AI Search, Pinecone, Qdrant, pgvector); - Atuar em deploys cloud-native (Azure/GCP) com containers (Docker) e orquestração; - Colaborar com times de produto, frontend e dados para entregar features end-to-end, eventualmente contribuindo no frontend (React/Next.js ou Streamlit) quando necessário.

Role Description CTG is seeking to fill a Senior Azure AI / MLOps Engineer position for our client. - Design and implement enterprise-scale Azure AI/ML platforms supporting production workloads. - Build and operationalize end-to-end MLOps pipelines using Azure ML, Microsoft Fabric, and CI/CD frameworks. - Architect secure, scalable Azure environments (VNets, RBAC, Entra ID, private endpoints, Key Vault). - Develop and deploy ML models with full lifecycle automation (training, validation, registration, monitoring, rollback). - Build Microsoft Fabric Lakehouse architectures using medallion design (Bronze/Silver/Gold). - Develop data ingestion pipelines integrating SAP, Snowflake, Azure SQL, IoT, and manufacturing systems. - Implement monitoring, observability, and alerting using Azure Monitor, Log Analytics, and Application Insights. - Design dashboards and operational reporting using Power BI and Azure Workbooks. - Enable real-time and near-real-time analytics using Microsoft Fabric Eventstreams and KQL databases. - Ensure security, compliance, governance, and cost optimization across AI/ML environments. Qualifications - Azure (Machine Learning, Fabric, Data Factory, Monitor, Key Vault, Networking) - Strong MLOps expertise (CI/CD, MLflow, model lifecycle management) - Python, PySpark, and data engineering pipelines - Infrastructure as Code (Terraform, Bicep, or ARM) - Cloud security, identity management, and enterprise architecture - Power BI (semantic models, dashboards) - Lakehouse architecture (medallion pattern) - Strong understanding of DevOps and production ML systems - Experience with real-time data processing (KQL, Eventstreams) preferred Requirements - 5+ years in cloud engineering, MLOps, data engineering, or AI platform roles - Proven experience building production-grade ML pipelines in Azure - Experience with enterprise data integrations (SAP, Snowflake, Azure SQL, IoT systems) - Experience implementing cloud governance, security, and monitoring frameworks - Background in manufacturing, supply chain, or industrial analytics preferred - Experience working in enterprise or Agile cross-functional teams Education - Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field - Microsoft Azure certifications strongly preferred: - Azure Solutions Architect Expert - Azure AI Engineer Associate - Azure Data Engineer Associate - Excellent verbal and written English communication skills and the ability to interact professionally with a diverse group are required. To Apply To be considered, please apply directly to this requisition using the link provided. Kindly forward this to any other interested parties. Thank you! The expected base salary for this position ranges from $89.00 to $94.00/hour. Salary offers are based on a wide range of factors including relevant skills, training, experience, education, market factors, and where applicable, licensure or certifications obtained. In addition to salary, a competitive benefit package is also offered.

Role Description As a Founding Applied AI Engineer at Valent, you will help design and build the core intelligence layer of the product. This role is focused on applying modern AI techniques to messy enterprise problems involving documents, retrieval, reasoning, workflow automation, and human-in-the-loop review. - Help build systems that extract structured information from supplier documents, specifications, certificates, declarations, questionnaires, audit evidence, and regulatory files. - Power AI-assisted workflows for document collection, evidence packet assembly, customer requests, and audit preparation. - Use retrieval, ranking, and document understanding to help users find the right evidence across fragmented sources. - Generate draft responses, summaries, checklists, and compliance artifacts that remain reviewable and auditable by humans. - Build evaluation pipelines to measure quality, reduce hallucination, and improve reliability over time. - Design AI workflows that know when to act autonomously and when to escalate to a human. - Integrate AI features into a polished enterprise product experience. Qualifications - Strong software engineering fundamentals and experience shipping production systems. - Hands-on experience building with LLMs, retrieval systems, document AI, agents, or applied machine learning systems. - Ability to take AI systems from prototype to production. - Comfortable working across the stack when needed. - Ability to write clean, maintainable code and make sound technical tradeoffs. - Care about reliability, observability, testing, and product quality. - Ability to reason from first principles about when AI should automate, assist, or stay out of the way. - Interest in solving messy operational problems, not just building polished demos. - Clear communication in writing and comfort working remotely. - Ability to overlap for part of the day with U.S. Eastern Time. - Desire to join early and help shape the foundation of a company. Requirements - Experience with LLM application development. - Familiarity with RAG systems and vector databases. - Experience in document parsing, OCR, extraction, or classification. - Knowledge of evaluation frameworks for AI systems. - Experience in workflow automation or agentic systems. - Background in enterprise SaaS. - Familiarity with compliance, supply chain, manufacturing, regulatory, or quality workflows. - Experience with React, TypeScript, Node.js, Python, Postgres, or cloud infrastructure. Benefits - Work remotely from India on a U.S.-based enterprise AI product. - Build AI into workflows where the value is operational, measurable, and immediate. - Join early enough to have meaningful influence over architecture, product, and culture. - Work on hard problems at the intersection of AI, enterprise workflow, compliance, and manufacturing. - Help define what modern compliance operations software should look like.