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

Onsite: Yes

Rate:$85-90/Hr On C2C

Must Have Skills

Skill 1 - Python backend development

Skill 2 - Agent-based / agent-oriented workflow development

Skill 3 - API development and system integration

Skill 4 - LLM-enabled application development (prompt & context management, structured outputs)

Skill 5 - Retrieval-based systems (vector search, indexing, embeddings)

Skill 6 - AWS cloud-native development

Skill 7 - CI/CD and environment management

Skill 8 - 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.

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Backend & Agent-Oriented Engineering

Build and maintain Python-based backend services supporting:

o Agent orchestration (supervisor/sub-agent patterns)

o APIs and system integrations

o 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:

o Prompt and context management

o Structured outputs and validation

Data Engineering for Retrieval-Based Systems

Design and operate retrieval pipelines to support grounding and context enrichment, including:

o Vector search and similarity retrieval

o Search and indexing solutions

o Object storage for source content and embeddings

o Caching for performance and scalability

Cloud-Native Delivery

Deploy and operate services primarily on AWS, following best practices for:

o IAM and security

o Scalability, resiliency, and availability

o 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:

o Translate use cases into technical designs and acceptance criteria

o Communicate trade-offs across quality, cost, risk, and delivery timelines