Senior Generative AI / LLM Engineer

Hybrid in Bellevue, WA, US • Posted 2 days ago • Updated 1 hour ago
Contract Independent
Contract W2
No Travel Required
Hybrid
Depends on Experience
Fitment

Dice Job Match Score™

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Job Details

Skills

  • LLM
  • Gen AI
  • RAG
  • Python
  • Pandas
  • Azure
  • ETL
  • LangChain
  • Machine Learning (ML)
  • Generative Artificial Intelligence (AI)

Summary

Job Description

Core Language & Architecture

• Python 3.11+

• Advanced type hints (PEP 484), static typing discipline

• Async programming (asyncio, async/await, async generators)

• aiohttp / httpx (async HTTP clients)

• Pydantic v2 (BaseModel, validation, settings management)

• Structured logging & tracing patterns

• Redis (pub/sub, TTL, async clients)

• REST API design & integration patterns

• Retry/backoff strategies (Tenacity)

• Concurrency patterns (parallel tool calls, task orchestration)

 

AI / LLM / Agent Systems

• LangGraph (state machines, conditional edges, checkpointing)

• LangChain 0.3.x (LLMChain, StructuredTool, retrievers, prompt templates)

• ReAct-style agent architectures

• Tool-based agent design (40+ tool environments)

• Azure OpenAI / OpenAI APIs (GPT-4o, deployment mgmt, rate limits, token budgeting)

• Prompt engineering (few-shot, structured output, JSON mode)

• PydanticOutputParser / structured LLM responses

• Guardrails / PII redaction patterns

• Memory abstractions for agents

• Langfuse (trace instrumentation, evaluation, prompt management)

• LLM fallback chains & error recovery

• RAG prompt grounding strategies

• LLM fine-tuning

• Neural Network training & tuning

• Traditional ML models (random forest, k-means clustering, linear regression, etc.)

• MCP development and consumption

 

Retrieval, Search & RAG Engineering

• Vector databases (Qdrant and/or Milvus)

• HNSW indexing parameters

• Filtering strategies

• Embedding pipelines (OpenAI ada-002 or equivalent)

• Batch embedding & re-indexing workflows

• Hybrid retrieval (BM25 + semantic)

• Score fusion strategies

• Cross-encoder reranking (BAAI/bge models)

• FastAPI-based inference services

• LangChain retriever abstractions

• RAG evaluation metrics:

o Faithfulness

o Relevance

o NDCG

o MRR

• Trace-level RAG evaluation (Langfuse)

 

Data Engineering & ETL

• Prefect 2.x / 3.x

o Flows, tasks, futures

o Deployments (YAML)

o Scheduling

• ETL/ELT design

o Schema evolution

o Query optimization

• OAuth authentication

• Warehouse/schema management

• PostgreSQL 16/17

o psycopg 3.x

o Connection pooling

o SQLAlchemy 2.x (ORM + asyncio)

o Alembic migrations

o Advanced SQL

o Multi-table JOINs

o CTEs

o Window functions

• Timezone conversion

• Pandas 2.x (complex multi-stage transformations)

• PyArrow / columnar formats

• Azure Blob Storage (azure-storage-blob)

• Document ingestion/parsing:

o Docling

o Unstructured

o python-docx

o python-pptx

 

DevOps & Platform

• Docker

• Linux fundamentals

 

PI & Enterprise Integrations

• OAuth 2.0 (client credentials flow, token lifecycle)

• MSAL (browser + service principal flows)

• Microsoft Graph API

• SharePoint

• Outlook

• Planner

• OneDrive

• Pagination

• App permissions

• ServiceNow REST API

• Table API

• Incident/change mgmt

• Bulk operations

• Splunk SDK

• Saved searches

• Async queries

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: 90887977
  • Position Id: 8940007
  • Posted 2 days ago
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