Data Scientist / AI Architect (Agentic AI & LLM Focus)

Role

Data Scientist / AI Architect (Agentic AI & LLM Focus)

Location

2-3 days/week in the client's Irvine office, 1 day in their downtown LA office, 1 day remote

Rate

$85-90/Hr On C2C

Must Have Skills

• Python backend development
• Agent-based / agent-oriented workflow development
• API development and system integration
• LLM-enabled application development (prompt & context management, structured outputs)
• Retrieval-based systems (vector search, indexing, embeddings)
• AWS cloud-native development
• CI/CD and environment management
• Observability (logging, metrics, tracing)

Context & Objective

We are engaging a software engineer to support the design and delivery of agent-based, AI-enabled workflows that integrate with enterprise systems. The contractor will work closely with internal teams and business stakeholders to translate use cases into robust, scalable solutions.

Backend & Agent-Oriented Engineering

• Build and maintain Python-based backend services supporting:
  • Agent orchestration (supervisor/sub-agent patterns)
  • APIs and system integrations
  • Multi-step, asynchronous workflows
• Apply strong engineering practices (testing, code quality, error handling).

AI / LLM-Enabled Application Development

• Deliver LLM-enabled features end to end, including:
  • Prompt and context management
  • Structured outputs and validation

Data Engineering for Retrieval-Based Systems

• Design and operate retrieval pipelines to support grounding and context enrichment, including:
  • Vector search and similarity retrieval
  • Search and indexing solutions
  • Object storage for source content and embeddings
  • Caching for performance and scalability

Cloud-Native Delivery

• Deploy and operate services primarily on AWS, following best practices for:
  • IAM and security
  • Scalability, resiliency, and availability
  • CI/CD and environment management

Integration & UX Enablement

• Integrate with enterprise tools and services via secure APIs and gateways.
• Support React-based front-end patterns and collaboration integrations to enable effective user experiences.

Observability & Operations

• Implement logging, metrics, and tracing across agent workflows, model calls, and integrations.
• Support incident diagnosis, performance tuning, and ongoing optimisation.

Working with the Business

• Engage directly with business stakeholders to:
  • Translate use cases into technical designs and acceptance criteria
  • Communicate trade-offs across quality, cost, risk, and delivery timelines