Explore 3 ai python software engineer jobs from 2 hiring companies in Hickory Withe, TN, US. Filter by remote, salary, experience, and company, then open any listing to apply.

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Ai Python Software Engineer Jobs

Crew Training International

Radians

Onsite

PURPOSE OF POSITION

Responsible for 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:  Master's degree preferred. Bachelor's in Computer Science, Data Science, AI, or related field with equivalent experience considered, or related field or equivalent practical experience.

Training and Experience:  3-7 years in backend development, AI systems, or related roles, with a focus on LLMs integration or retrieval systems.

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:

<ul>
	<li>Mastery in Python, including experience with modern practices in structuring, testing, and maintaining codebases.</li>
</ul>

<ul>
	<li>Orchestrated Retrieval-Augmented Generation (RAG) systems, including document chunking, embedding, vector search, and grounded context construction.</li>
</ul>

<ul>
	<li>Expertise with PostgreSQL and pgvector, including schema design and structured retrieval over relational data.</li>
</ul>

<ul>
	<li>Robust operational understanding with SQL query generation, particularly in the context of semantic or hybrid retrieval.</li>
</ul>

<ul>
	<li>Comprehensive background integrating and orchestrating LLMs, with a focus on prompt templating, tool usage, and response parsing.</li>
</ul>

<ul>
	<li>Familiarity with Google ADK or equivalent frameworks for LLM scaffolding and orchestration.</li>
</ul>

<ul>
	<li>Proficient in utilizing unstructured and structured data, including ingestion from PDFs, DOCX, Markdown, HTML, and APIs.</li>
</ul>

<ul>
	<li>Experience deploying and debugging LLM systems, including containerization (Docker), API-based LLM integration (e.g., Ollama or vLLM), and environment configuration.</li>
</ul>

Preferred Skills

<ul>
	<li>Background with graph-enhanced retrieval, using tools like Neo4j or ArangoDB, and an understanding of when and how to apply knowledge graphs to improve LLM grounding.</li>
</ul>

<ul>
	<li>Versed in model adaptation techniques, including LoRA, QLoRA, or PEFT approaches for fine-tuning or personalization.</li>
</ul>

<ul>
	<li>Expert in designing and implementing advanced prompt optimization frameworks, including developing automated evaluation systems and troubleshooting complex failure modes to enhance AI model performance and reliability.</li>
</ul>

<ul>
	<li>Proven ability to design end-to-end hybrid search and reranking pipelines, such as ColBERT, BGE rerankers, or commercial tools like Cohere Rerank.</li>
</ul>

<ul>
	<li>Expertise with infrastructure optimizations, such as autoscaling (KEDA, HPA), Redis caching layers, or efficient streaming and batching.</li>
</ul>

<ul>
	<li>Demonstrated skill in safe deployment practices, including prompt injection mitigation and handling of sensitive or regulated data.</li>
</ul>

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

DUTIES &amp; RESPONSIBILITIES

<ul>
	<li>Design and implement end-to-end RAG architectures, including document ingestion, chunking, embedding generation, vector indexing, query planning, retrieval, and response synthesis.</li>
</ul>

<ul>
	<li>Evaluate and integrate LLMs, embedding models, and vector databases to support efficient and accurate retrieval and generation.</li>
</ul>

<ul>
	<li>Design and implement scaffolding and orchestration around LLMs, including prompt templating, tool invocation, evaluation harnesses, and safety guards.</li>
</ul>

<ul>
	<li>Develop data processing pipelines for structured and unstructured content (PDF, DOCX, HTML, Markdown, databases, APIs); implement normalization, deduplication, PII redaction, and metadata enrichment.</li>
</ul>

<ul>
	<li>Implement and optimize retrieval strategies and context construction (citation, source attribution, grounding).</li>
</ul>

<ul>
	<li>Adapt retrieval and embedding strategies to domain-specific taxonomies, ontologies, or structured schemas; support contextual retrieval from hierarchical or relational sources.</li>
</ul>

<ul>
	<li>Productionize LLM-based systems: containerize components (Docker), deploy orchestration via Kubernetes or serverless platforms, implement observability (OpenTelemetry, logging, tracing), and manage configuration.</li>
</ul>

<ul>
	<li>Measure and improve quality: define offline and online evals, golden datasets, A/B tests, hallucination detection, toxicity filters, and guardrails.</li>
</ul>

<ul>
	<li>Optimize performance and cost: batching, caching, streaming, and efficient context management.</li>
</ul>

<ul>
	<li>Implement security, privacy, and compliance best practices including access controls, injection defense, and safe data handling.</li>
</ul>

<ul>
	<li>Develop solutions that can run entirely on-premise or in air-gapped environments, prioritizing data sovereignty and privacy.</li>
</ul>

<ul>
	<li>Various other duties in direct support of accomplishment of primary duties listed.</li>
</ul>

SUPERVISORY/MANAGEMENT RESPONSIBILITY

None

AI Developer

3-7 years of backend development or AI systems experience; strong Python, SQL, and data tooling; experience with LLMs in production; ability to build robust pipelines for context-aware LLM workflows.

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:

<ul>
	<li>Proficiency in Python, including experience with modern practices in structuring, testing, and maintaining codebases.</li>
</ul>

<ul>
	<li>Experience with Retrieval-Augmented Generation (RAG) systems, including document chunking, embedding, vector search, and grounded context construction.</li>
</ul>

<ul>
	<li>Hands-on experience with PostgreSQL and pgvector, including schema design and structured retrieval over relational data.</li>
</ul>

<ul>
	<li>Strong familiarity with SQL query generation, particularly in the context of semantic or hybrid retrieval.</li>
</ul>

<ul>
	<li>Experience integrating and orchestrating LLMs, with a focus on prompt templating, tool usage, and response parsing.</li>
</ul>

<ul>
	<li>Familiarity with Google ADK or equivalent frameworks for LLM scaffolding and orchestration.</li>
</ul>

<ul>
	<li>Comfort working with unstructured and structured data, including ingestion from PDFs, DOCX, Markdown, HTML, and APIs.</li>
</ul>

<ul>
	<li>Experience deploying and debugging LLM systems, including containerization (Docker), API-based LLM integration (e.g., Ollama or vLLM), and environment configuration.</li>
</ul>

Preferred Skills

<ul>
	<li>Experience with graph-enhanced retrieval, using tools like Neo4j or ArangoDB, and an understanding of when and how to apply knowledge graphs to improve LLM grounding.</li>
</ul>

<ul>
	<li>Knowledge of model adaptation techniques, including LoRA, QLoRA, or PEFT approaches for fine-tuning or personalization.</li>
</ul>

<ul>
	<li>Familiarity with prompt optimization strategies, including prompt evaluation and failure case analysis.</li>
</ul>

<ul>
	<li>Basic understanding of hybrid search and reranking pipelines, such as ColBERT, BGE rerankers, or commercial tools like Cohere Rerank.</li>
</ul>

<ul>
	<li>Experience with infrastructure optimizations, such as autoscaling (KEDA, HPA), Redis caching layers, or efficient streaming and batching.</li>
</ul>

<ul>
	<li>Familiarity with safe deployment practices, including prompt injection mitigation and handling of sensitive or regulated data.</li>
</ul>

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

DUTIES &amp; RESPONSIBILITIES

<ul>
	<li>Design and implement end-to-end RAG architectures, including document ingestion, chunking, embedding generation, vector indexing, query planning, retrieval, and response synthesis.</li>
</ul>

<ul>
	<li>Evaluate and integrate LLMs, embedding models, and vector databases to support efficient and accurate retrieval and generation.</li>
</ul>

<ul>
	<li>Design and implement scaffolding and orchestration around LLMs, including prompt templating, tool invocation, evaluation harnesses, and safety guards.</li>
</ul>

<ul>
	<li>Develop data processing pipelines for structured and unstructured content (PDF, DOCX, HTML, Markdown, databases, APIs); implement normalization, deduplication, PII redaction, and metadata enrichment.</li>
</ul>

<ul>
	<li>Implement and optimize retrieval strategies and context construction (citation, source attribution, grounding).</li>
</ul>

<ul>
	<li>Adapt retrieval and embedding strategies to domain-specific taxonomies, ontologies, or structured schemas; support contextual retrieval from hierarchical or relational sources.</li>
</ul>

<ul>
	<li>Productionize LLM-based systems: containerize components (Docker), deploy orchestration via Kubernetes or serverless platforms, implement observability (OpenTelemetry, logging, tracing), and manage configuration.</li>
</ul>

<ul>
	<li>Measure and improve quality: define offline and online evals, golden datasets, A/B tests, hallucination detection, toxicity filters, and guardrails.</li>
</ul>

<ul>
	<li>Optimize performance and cost: batching, caching, streaming, and efficient context management.</li>
</ul>

<ul>
	<li>Implement security, privacy, and compliance best practices including access controls, injection defense, and safe data handling.</li>
</ul>

<ul>
	<li>Develop solutions that can run entirely on-premise or in air-gapped environments, prioritizing data sovereignty and privacy.</li>
</ul>

<ul>
	<li>Various other duties in direct support of accomplishment of primary duties listed.</li>
</ul>

SUPERVISORY/MANAGEMENT RESPONSIBILITY

None

Junior AI Developer

0–2 years in software development or data engineering; strong Python; experience with RAG, PostgreSQL/pgvector; LLM tooling; able to obtain Secret clearance.

Description&nbsp;&nbsp;Role summaryYou are the primary builder for the AI Integration team's roadmap. You take scoped initiatives from the AI Adoption Analyst and Department Head, architect them, and ship them into production — end-to-end. That includes application code, the data pipelines that feed them, the integrations with our ERP and adjacent systems, and the observability to know they're working.This role is explicitly a hybrid: you'll write production C#/.NET application code the majority of the time, but you'll also write Python for data work, SQL for ERP integrations, and occasional infrastructure scripts. You're comfortable switching contexts and don't treat "that's not my stack" as a reason to stop. What you'll own· Application development — .NET 8 / C# against our existing platform (Arc on Blazor Server). Extend existing modules, build new ones, integrate with Prophet21 via both its API and direct SQL.· LLM integration work — production-grade Anthropic (and possibly OpenAI) API usage. Prompt design, tool use, conversation state, context management, cost tracking, fallback/retry behavior. Most of the team's tools have an AI reasoning layer somewhere.· Data pipelines — ingestion from email (Microsoft Graph), PDFs (PyMuPDF, vision models), and external vendors. Transformation and matching logic. Storage in SQL Server.· Quality and drift monitoring — as the person closest to the model calls, you own the metrics that show whether the AI is still producing good output. Accuracy dashboards, eval frameworks, regression checks when prompts or models change.· Reliability engineering — Windows services, background workers, error handling, logging discipline, on-call awareness. Not DevOps-heavy, but you don't ship and wash your hands.· Code review partner — collaborate on design with the Department Head and (as we grow) mentor the next engineer on the team.Requirements&nbsp;&nbsp;Must-have qualifications· 5+ years of production software engineering experience shipping features people use every day.· Strong proficiency in one of: .NET/C#, Python, Java/Kotlin, TypeScript/Node — with a willingness to cross-train into the others. Our stack is primarily .NET 8 and Python; prior .NET experience is a strong plus but a great engineer in another stack can ramp.· SQL proficiency: you can write non-trivial queries, read execution plans, and join across multiple sources without holding your nose.· At least one production system you've shipped that uses an LLM API (Anthropic, OpenAI, Cohere, Mistral — doesn't matter which). You understand the failure modes: rate limits, hallucinations, cost escalation, context window management, tool use loops.· Comfortable working with data that isn't clean: document extraction, fuzzy matching, multi-source deduplication, schema mismatches.· On-site at Radians Memphis HQ, full-time. ·Nice-to-have qualifications· Microsoft Graph API experience (Teams integrations, Outlook integrations, SharePoint).· Prophet21 or similar distribution/manufacturing ERP integration experience.· Prompt engineering or eval-framework experience (Langsmith, Promptfoo, internal harnesses).· Experience with document intelligence pipelines (intake ? extraction ? classification ? downstream action).· Azure or AWS cloud experience.· Open-source contributions or public technical writing.

SENIOR AI ENGINEER (ONSITE ROLE)

Senior AI Engineer

5+ years of production software engineering; proficient in .NET/C#, Python, or JavaScript/TypeScript; strong SQL; experience with at least one LLM API; on-site in Memphis.

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