Junior AI Developer

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Requisition #

_COMPANY_1.3

Job Title

Junior AI Developer

Job Type

Full-time

Location

Corporate - TN US
Memphis, TN 38119 US (Primary)

Category

Operations

Job Description

PURPOSE OF POSITION

Assist with model integration, data pipelines, retrieval infrastructure, and the engineering scaffolding required to ship reliable, secure, and cost-effective Artificial Intelligence (AI) features. This role ensures the delivery of production-grade Large Language Model (LLM) systems that meet real-world demands for performance, cost-efficiency, and governance.

MINIMUM QUALIFICATIONS

Education: Bachelor’s Degree in Computer Science, Data Science, AI, or related field is preferred, but not required. Equivalent practical experience, including boot camps, certifications, or self-directed learning, is also valued.

Training and

Experience:

0–2 years of professional experience in software development, data engineering, machine learning, or backend development.

General

Skills:

Must have strong software engineering fundamentals and a deep understanding of working with LLMs in production environments. The ideal candidate brings hands-on experience with Python and modern data tooling and is comfortable building robust pipelines that connect unstructured content, structured data, and retrieval systems to power context-aware LLM workflows. You should demonstrate fluency in the design and reasoning of data movement processes, including ingestion, preprocessing, vector indexing, and query generation.

Experience working with both open-weight and API-based large language models is also essential. This role requires a practical mindset, a strong command of SQL and retrieval strategies over relational data, and the ability to experiment, evaluate, and iterate toward scalable, cost-effective, and trustworthy AI features.

Required Skills:

• Proficiency in Python, including experience with modern practices in structuring, testing, and maintaining codebases.
• Experience with Retrieval-Augmented Generation (RAG) systems, including document chunking, embedding, vector search, and grounded context construction.
• Hands-on experience with Postgre
  
  SQL and pgvector, including schema design and structured retrieval over relational data.
• Strong familiarity with SQL query generation, particularly in the context of semantic or hybrid retrieval.
• Experience integrating and orchestrating LLMs, with a focus on prompt templating, tool usage, and response parsing.
• Familiarity with Google ADK or equivalent frameworks for LLM scaffolding and orchestration.
• Comfort working with unstructured and structured data, including ingestion from PDFs, DOCX, Markdown, HTML, and APIs.
• Experience deploying and debugging LLM systems, including containerization (Docker), API-based LLM integration (e.g., Ollama or vLLM), and environment configuration.

Preferred Skills

• Experience with graph-enhanced retrieval, using tools like Neo4j or Arango
  
  DB, and an understanding of when and how to apply knowledge graphs to improve LLM grounding.
• Knowledge of model adaptation techniques, including LoRA, QLoRA, or PEFT approaches for fine-tuning or personalization.
• Familiarity with prompt optimization strategies, including prompt evaluation and failure case analysis.
• Basic understanding of hybrid search and reranking pipelines, such as ColBERT, BGE rerankers, or commercial tools like Cohere Rerank.
• Experience with infrastructure optimizations, such as autoscaling (KEDA, HPA), Redis caching layers, or efficient streaming and batching.
• Familiarity with safe deployment practices, including prompt injection mitigation and handling of sensitive or regulated data.

Clearance: Must be able to obtain/maintain a Secret clearance. Prefer holds an active Secret clearance.

DUTIES & RESPONSIBILITIES

• Design and implement end-to-end RAG architectures, including document ingestion, chunking, embedding generation, vector indexing, query planning, retrieval, and response synthesis.
• Evaluate and integrate LLMs, embedding models, and vector databases to support efficient and accurate retrieval and generation.
• Design and implement scaffolding and orchestration around LLMs, including…