Design and deploy enterprise Generative AI systems across the 7-layer stack: data ingestion, preprocessing, model selection, orchestration, inference, integration, and application. Lead model selection/tuning (commercial and open-source), establish LLMOps and observability, integrate AI with cloud platforms, enforce data protection, manage hallucinations/security, and act as onshore technical lead for client workshops and stakeholder alignment.
Design and deploy enterprise-grade AI solutions (LLMs, RAG, agents) by selecting appropriate models, building data pipelines, and integrating them with cloud platforms (AWS, Azure, GCP). Lead technical strategies across the standard 7-layer GenAI stack (from data ingestion to application interfaces), ensure scalability, manage AI security/hallucinations, and bridge business needs with engineering teams.
Responsibilities- System Design & Architecture: Architect end-to-end Generative AI systems by operationalizing the 7-layer AI architecture (Data Sources, Preprocessing, Model Selection, Orchestration, Inference, Integration, and Application).
- Model Selection & Tuning: Evaluate and select cutting-edge commercial (e.g., GPT-4) and open-source models, and fine-tune models for domain-specific use cases.
- LLMOps, Observability & Pipelines: Establish LLMOps standards for model versioning and CI/CD. Implement foundational observability (OBS) layers using tools like Datadog, Splunk, or Prometheus to monitor system health, API latency, and basic application metrics.
- Integration & Data Protection: Integrate AI solutions with existing APIs while enforcing core data protection measures, including Role-Based Access Control (RBAC), data encryption in transit, and basic PII (Personally Identifiable Information) masking to manage hallucinations and adversarial attacks.
- Strategic Leadership: Collaborate with stakeholders to map business challenges to AI solutions and establish AI governance frameworks.
- Client Consulting: Act as the primary onshore technical liaison, facilitating client workshops, requirements gathering, and translating business pain points into technical AI blueprints.
- Consulting Skills: Exceptional client-facing communication skills; proven ability to present complex technical concepts to business stakeholders.
- Technical Expertise: Deep knowledge of NLP, Python, deep learning frameworks (PyTorch/TensorFlow), and orchestration tools (LangChain, Autogen).
- Cloud & Data Systems: Extensive hands-on experience with AI services on AWS, Azure, or GCP. Expertise in vector databases (e.g., Pinecone, Milvus) and embedding techniques.
- Qualifications: Bachelor’s / Master’s in Computer Science, AI, Data Science, or related field; 8–15 years in software engineering, ML, or AI roles, with demonstrable onshore consulting experience.
Similar Jobs
Cloud • Information Technology • Consulting
Leads enterprise Azure Virtual Desktop and Azure architecture engagements, advising executive stakeholders and translating business requirements into secure, scalable, cost-effective solutions. Defines AVD standards, reference architectures, roadmaps, capacity and cost optimization strategies, and implementation plans. Partners with sales, engineering, delivery, and operations teams throughout the architecture lifecycle while mentoring technical teams. Requires deep experience with Azure, AVD, identity, networking, infrastructure as code, automation, and enterprise EUC/VDI migrations.
Top Skills:
Active DirectoryArm TemplatesAWSAzure ComputeAzure FilesAzure MonitorAzure Netapp FilesAzure PolicyAzure StorageAzure Virtual DesktopAzure Virtual NetworksBicepCi/CdCitrixFslogixGoogle Cloud PlatformLog AnalyticsAzureMicrosoft Defender For EndpointMicrosoft Entra IdMicrosoft IntuneNerdioPowershellRemote Desktop ServicesTerraformVmware Horizon
Artificial Intelligence • Fintech • Software • Travel
Own the full lifecycle and growth of strategic channel partnerships across associations, fintech, marketplaces, and distribution platforms. Drive revenue and GBV through performance optimization, QBRs, commercial negotiations, partner enablement, tier progression, and expansion initiatives. Build playbooks, health scoring, and scalable channel infrastructure while translating partner needs into product requests and coordinating cross-functional issue resolution.
Top Skills:
CrossbeamExcelGoogle Sheets
Artificial Intelligence • Cloud • Payments • Software • Business Intelligence • Generative AI • Automation
Own customer support cases for Ivans digital products from initial contact through resolution. Troubleshoot configuration, data, integration, API, and potential product issues; perform root-cause analysis; guide onboarding and setup; document recurring solutions; and collaborate with Product, Engineering, and integration partners. Translate technical findings into clear customer communication and well-documented escalations.
Top Skills:
APIsIvans Digital ProductsSaaSStructured Data Formats
What you need to know about the Austin Tech Scene
Austin has a diverse and thriving tech ecosystem thanks to home-grown companies like Dell and major campuses for IBM, AMD and Apple. The state’s flagship university, the University of Texas at Austin, is known for its engineering school, and the city is known for its annual South by Southwest tech and media conference. Austin’s tech scene spans many verticals, but it’s particularly known for hardware, including semiconductors, as well as AI, biotechnology and cloud computing. And its food and music scene, low taxes and favorable climate has made the city a destination for tech workers from across the country.
Key Facts About Austin Tech
- Number of Tech Workers: 180,500; 13.7% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Dell, IBM, AMD, Apple, Alphabet
- Key Industries: Artificial intelligence, hardware, cloud computing, software, healthtech
- Funding Landscape: $4.5 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Live Oak Ventures, Austin Ventures, Hinge Capital, Gigafund, KdT Ventures, Next Coast Ventures, Silverton Partners
- Research Centers and Universities: University of Texas, Southwestern University, Texas State University, Center for Complex Quantum Systems, Oden Institute for Computational Engineering and Sciences, Texas Advanced Computing Center
