Generative AI Developer

About Bay Rock Labs
At Bay Rock Labs, we pioneer innovative tech solutions that drive business transformation. As a leading product engineering firm based in Silicon Valley, we provide full-cycle product development, leveraging cutting-edge technologies in AI, ML, and data analytics. Our collaborative, inclusive culture fosters professional growth and work-life balance. Join us to work on ground-breaking projects and be part of a team that values excellence, integrity, and innovation.

Together, let's redefine what's possible in technology.

We are seeking a hands-on Generative AI Developer to be the primary builder of our production-grade multi-agent applications. This critical role is responsible for implementing complex, stateful workflows using Lang Graph
, integrating them deeply with Snowflake Cortex AI and Snowpark
, and ensuring all agents are highly performant, reliable, and compliant through rigorous testing, observability, and strategic LLM fine-tuning and agent training
.

• Agent Implementation: Design, build, and deploy specialized AI agents (e.g., Data Agent, Validation Agent, Assignment Agent) using Python and best practices for modular, reusable code.
• Lang Graph Mastery: Implement complex, long-running, and conditional Multi-Agent workflows using Lang Graph, handling state management
  , human-in-the-loop steps
  , and robust error handling across critical business processes.
• Prompt & Reasoning: Develop and optimize production-ready prompt templates, manage context windows, and define tool-calling schemas to enhance the agents' decision-making and reasoning capabilities.
• LLM Fine tuning: Own the process of fine tuning open-source and proprietary foundation models (e.g., via Cortex Fine-Tuning or external platforms) for specific domain tasks (e.g., structured data extraction, classification, complex reasoning) to improve agent accuracy and reduce inference costs.
• Agent Training & Adaptation: Implement strategies to systematically train and adapt agent behavior based on real-world workflow data, focusing on improving tool-use, reasoning chains, and decision-making accuracy within the Lang Graph framework.
• Data Curation: Collaborate with data science and engineering teams to curate high-quality, labeled datasets necessary for both pre-training and reinforcement learning techniques for agent improvement.

• Snowflake Cortex Integration: Develop custom Agent Tools that interface directly with the Snowflake data layer, specifically leveraging:
• Cortex LLM Functions (CORTEX.COMPLETE) for flexible reasoning tasks.
• Cortex Analyst to execute optimized Text-to-SQL queries for data retrieval and reporting.
• RAG Implementation: Build and optimize the RAG pipeline that allows agents to securely retrieve contextual information (e.g., policy documents, historical contract terms) from Snowflake to ground their responses.

• Evaluation Frameworks: Implement systematic testing and evaluation using the Lang Smith ecosystem to measure agent performance metrics such as accuracy, groundedness, latency, and cost
  , tracking improvements post-fine tuning and training.
• AI Observability: Integrate logging, tracing, and analytics across the entire Lang Graph workflow to provide the auditability and transparency necessary for a critical enterprise application.
• Production Readiness: Assist the architecture team in preparing agents for deployment, including containerization (e.g., Docker) and integration into the production environment (e.g., Snowflake Container Services).

• 6+ years of professional software development experience, with a focus on Python in a data or AI context.
• Hands-on experience building, testing, and product ionizing Generative AI applications.
• Expert-level proficiency with Lang Chain and Lang Graph for building complex, stateful multi-agent systems.
• Demonstrated ability to build custom tools and integrate them with Snowflake Cortex AI and Snowpark.
• Strong practical experience in LLM fine tuning, including understanding of data preparation, popular techniques (e.g., LoRA), and evaluation of finetuned models.
• Strong practical understanding of RAG (Retrieval-Augmented Generation), semantic search, vector databases, and managing LLM context/memory.
• Experience with evaluation and observability tools for Gen AI (e.g.,
  Lang Smith
  , Tru Lens, Weights & Biases).
• Familiarity with containerization (Docker) and deployment patterns for AI services.
  Pay rate: 25
  
  LPA