Design and deploy end-to-end Generative AI systems across the 7-layer stack. Select and fine-tune models, build LLMOps pipelines, implement observability, integrate with cloud platforms and APIs, enforce data protection, and lead stakeholder workshops to translate business needs into scalable, secure AI solutions.
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.
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What you need to know about the Chicago Tech Scene
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