Gen AI Developer (Long term Contract)

Key Responsibilities :

A. Multi-Agent Development & Orchestration

• Agent Implementation:

  Design, build, and deploy specialized AI agents (e.g., Data Agent, Validation Agent, Assignment Agent) using Python and best practices for modular, re-usable code.

• LangGraph Mastery:

  Implement complex, long-running, and conditional Multi-Agent workflows using LangGraph, 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.

B. LLM & Agent Optimization (Finetuning & Training)

• LLM Finetuning:

  Own the process of

  finetuning 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 LangGraph 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.

C. Tooling and Snowflake Cortex Integration

• 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.

D. Evaluation, Observability & Deployment

• Evaluation Frameworks:

  Implement systematic testing and evaluation using the

  LangSmith

  ecosystem to measure agent performance metrics such as

  accuracy, groundedness, latency, and cost

  , tracking improvements post-finetuning and training.

• AI Observability:

  Integrate logging, tracing, and analytics across the entire LangGraph 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).

Required Skills & Qualifications

• 6+ years

  of professional software development experience, with a focus on

  Python

  in a data or AI context.

• Hands-on experience

  building, testing, and productionizing Generative AI applications.

• Expert-level proficiency

  with

  LangChain and LangGraph

  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 finetuning,

  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.,

  LangSmith

  , TruLens, Weights & Biases).
• Familiarity with containerization (Docker) and deployment patterns for AI services.