What are the responsibilities and job description for the Senior Python AI Engineer - Only W2 position at Info Dinamica Inc?
Role: Senior Python AI Engineer
Location: Remote (US)
Job Type: W2 Contract
Experience Required: 10 years
JD:
-Knowledge Base Infrastructure
We are hiring a Python Platform Engineer to operate and evolve the infrastructure behind an enterprise knowledge base platform that is moving from a Confluence-focused RAG chatbot into a broader agentic knowledge system. Today, the platform supports Confluence/GitHub ingestion chunking pgvector RAG retrieval FastAPI serving. Over the next phase, we are expanding toward hybrid retrieval (vector sparse graph), multi-source ingestion, evaluation pipelines, agent infrastructure, harness and shared chat platform primitives.
What You'll Own:
- Architect and implement backend services in Python 3.11, FastAPI, Pydantic, SQLAlchemy async, and asyncpg
- Design retrieval and orchestration trade-offs around quality, latency, cost, safety, and operational simplicity
- Build production-grade agent runtime capabilities: memory boundaries, tool sandboxing, permissions, and budget controls
- Improve answer grounding, failure analysis, and citation enforcement rather than optimizing for demo behavior
- Create observability and operational feedback loops with OpenTelemetry, PrometheGrafana, Docker/Helm, and GitHub Actions
- Work closely with product and engineering partners to support multiple conversational surfaces through one knowledge platform
- Ingestion infrastructure across current and future content sources
- Observability across application, pipeline, database, and model-serving behavior
- Cost, latency, throughput, and failure-mode management for AI-heavy workloads
- Release workflows that validate AI behavior changes, not just code compilation
Qualifications:
- Strong hands-on experience with Python in platform, automation, or infrastructure-heavy environments
- Experience building CLI tools using Python, Golang or Rust.
- Hands-on experience with LangGraph, LangChain, pgvector, and modern retrieval pipelines
- Experience designing evaluation frameworks for LLM-backed systems, including regression detection and quality measurement
- Strong experience with Docker, Helm, GitHub Actions, and Kubernetes-oriented workflows
- Familiarity with the operational characteristics of embedding pipelines, vector search, and LLM-backed systems
- Strong observability skills across metrics, tracing, dashboards, alerting, and log analysis
- Experience with ingestion, ETL, or content-processing pipelines at scale
- Ability to think in terms of reliability, cost, latency, throughput, and recovery
Nice to Have:
- Experience with Qdrant, Neo4j, or other vector/graph infrastructure
- Experience supporting RAG, search, evaluation, or agent platforms
- Experience in enterprise or regulated environments
- Familiarity with Vault, Splunk, Artifactory, ECR
- Comfort using AI-assisted engineering workflows in day-to-day work