Must Haves
- Proven experience building and deploying fine-tuned LLMs in production environments
- Hands-on experience with Google Vertex AI and Gemini models
- Experience performing supervised fine-tuning (SFT) using LoRA adapters
- Strong expertise creating and validating JSONL training datasets
- Experience building multi-source training data pipelines from structured and unstructured data
- Ability to diagnose and resolve model tuning issues including overfitting, underfitting, data imbalance, and model drift
- Experience with vector embeddings, semantic search, and RAG architectures
- Strong C# and .NET development experience
- Experience building Core REST APIs
- Experience developing React applications and modern JavaScript frontends
- Strong SQL skills and experience integrating Oracle and SQL Server data sources
- Experience with MongoDB Atlas and Vector Search
- Cloud experience within Google Cloud Platform including Vertex AI, Cloud Storage, and related services
- Experience with Docker, CI/CD pipelines, GitHub Actions, and containerized deployments
- Ability to independently troubleshoot cloud, networking, deployment, and database connectivity issues
- Comfortable owning projects end-to-end from design through deployment
Pluses
- Experience with Gemini 2.5 Flash specifically
- Experience implementing lightweight classifier models for AI routing and orchestration
- Experience with text-embedding-005
- Familiarity with two-pass continuous fine-tuning methodologies
- Experience with training data provenance tracking and audit trail implementation
- Experience with real-time AI response streaming using Server-Sent Events (SSE)
- Experience with Vite, Prime React, or similar frontend frameworks
- Experience implementing model versioning, rollback strategies, and endpoint lifecycle management
- Prior experience building AI-powered chat assistants or conversational applications
- Experience optimizing AI latency, token consumption, and inference costs
Day-to-day
- Design and execute LLM fine-tuning workflows within Vertex AI
- Build and maintain training data pipelines leveraging business rules, SQL schemas, SOPs, and expert-curated datasets
- Monitor model performance, identify drift, and initiate retraining efforts as needed
- Analyze training metrics, loss curves, and tuning outcomes to improve model quality
- Build and enhance vector search and semantic retrieval capabilities
- Develop and maintain .NET APIs supporting AI-powered applications
- Build AI chat experiences and supporting React-based user interfaces
- Implement streaming AI responses and session-aware conversational workflows
- Integrate Oracle, SQL Server, and MongoDB data sources into AI pipelines
- Manage deployments and CI/CD workflows through GitHub Actions and Docker
- Monitor Vertex AI jobs, model endpoints, and cloud infrastructure
- Troubleshoot production issues across application, cloud, network, and database layers
- Collaborate with stakeholders to deliver new AI features and continuously improve platform performance
- Drive solutions from design and development through testing, deployment, and production support
Rate:
Not specified
Location:
Remote
IR35 Status:
Outside
Remote Status:
Remote
Industry:
AI & Machine Learning
Seniority Level:
Not Specified