LLM Engineer

Talent Software Services

United States

Job Details

Job Title: LLM Engineer
Location: Cincinnati, OH
Work Location: Remote - USA
Duration: 1 year
Experience Required: 8+ years
Role Category: AI and Automation
Education: Any degree
Start Date: 15-September-2026

Role Overview

Design, build, optimize, deploy, and operate Large Language Model (LLM) and Small Language Model (SLM) capabilities.
Build secure, reliable, reusable, and enterprise-ready AI capabilities.
Support:
- Agentic AI workflows
- AI for SDLC
- Knowledge retrieval
- Model evaluation
- Private AI hosting
- AgentOps
Work closely with:
- Principal AI Architect
- AI Engineering Lead
- Platform Engineers
- Security teams
- Enterprise Architecture
- Product Owners
- Domain teams

Must-Have Technical Skills

LLM & Generative AI

Large Language Models (LLMs)
Small Language Models (SLMs)
Prompt Engineering
Context Engineering
Retrieval-Augmented Generation (RAG)
Embeddings
Semantic Search
Agentic AI Patterns
Multi-Agent Workflows
Tool Calling
Function Calling
Model Evaluation
LLM Observability

Model Engineering

Fine-Tuning
Supervised Fine-Tuning
LoRA
QLoRA
Quantization
Distillation
Model Compression
Synthetic Data Generation
Model Benchmarking
Model Selection
Model Routing

Model Hosting & Serving

Private LLM Hosting
On-Prem Model Deployment
GPU-Based Inference
Model Serving APIs
High-Availability Inference
Autoscaling
Load Balancing
Caching
Batch and Real-Time Inference

AI Infrastructure & Frameworks

Kubernetes
Docker
Kubeflow
KServe
Ray Serve
MLflow
Hugging Face
Transformers
PyTorch
PEFT
DeepSpeed
NVIDIA NIM
Triton Inference Server
TensorRT-LLM
vLLM
TGI
SGLang

Programming & Engineering

Python
TypeScript or JavaScript
REST APIs
Microservices
CI/CD
GitHub or Azure DevOps
API Design
Distributed Systems
Cloud-Native Engineering
Test Automation

Data & Knowledge Systems

Vector Databases
Knowledge Graphs
Document Processing
Metadata Management
Data Pipelines
Object Storage
Enterprise Search
Structured and Unstructured Data Integration

Roles & Responsibilities

LLM Application Engineering

Build enterprise-grade LLM-powered applications and intelligent agent capabilities.
Design reusable LLM patterns, services, APIs, and accelerators.
Develop model interaction patterns for:
- Reasoning
- Summarization
- Classification
- Extraction
- Planning
- Decision support
Build reusable prompt, context, retrieval, memory, and evaluation components.
Support AI-for-SDLC agents across:
- Requirements
- Design
- Coding
- Testing
- Security Review
- Deployment
- Operations
Convert AI use cases into scalable production solutions.

Agent Factory Intelligence Layer

Build core intelligence services for enterprise agents.
Develop reusable capabilities for:
- Planning
- Task decomposition
- Reasoning
- Tool usage
- Agent collaboration
Enable agent-to-agent interaction and multi-agent orchestration.
Integrate LLMs with:
- Agent runtimes
- Tool registries
- Workflow engines
- MCP-based gateways
Support human-in-the-loop, approval, escalation, and feedback workflows.
Improve agent quality, accuracy, safety, and task completion.

Prompt & Context Engineering

Design reusable prompt engineering standards, templates, and libraries.
Create:
- System prompts
- Task prompts
- Role prompts
- Guardrail prompts
- Evaluation prompts
Develop context engineering strategies for better grounding, relevance, and personalization.
Optimize:
- Token usage
- Context windows
- Memory injection
- Retrieval inputs
Establish prompt versioning, testing, and governance practices.

Retrieval-Augmented Generation (RAG)

Design and implement enterprise RAG architectures.
Build retrieval pipelines using:
- Enterprise documents
- Knowledge repositories
- Structured data
- Metadata
Optimize:
- Chunking
- Embeddings
- Indexing
- Ranking
- Reranking
- Retrieval strategies
Improve grounding, citation quality, precision, recall, and factual accuracy.
Build reusable retrieval services for agents and business domains.
Partner with data and knowledge management teams to onboard trusted data sources.

LLM / SLM Model Engineering

Evaluate, build, fine-tune, deploy, and optimize LLMs and SLMs.
Support domain-specific model development using approved datasets.
Build supervised fine-tuning and model adaptation pipelines.
Apply:
- LoRA
- QLoRA
- Distillation
- Quantization
- Model compression
Evaluate commercial, open-source, and internally hosted models.
Select models based on:
- Accuracy
- Latency
- Cost
- Data residency
- Security
- Operational requirements

Private AI & On-Prem Hosting

Build and support private AI capabilities for LLM/SLM hosting.
Deploy models across:
- On-premises
- Hybrid
- Private cloud environments
Support GPU-enabled model hosting.
Optimize latency, throughput, concurrency, resiliency, and GPU utilization.
Build secure inference endpoints for internal applications and agents.
Support air-gapped and restricted AI environments.
Partner with infrastructure and platform teams on private AI hosting.

Model Serving & Inference Optimization

Implement scalable model serving using modern inference frameworks.
Build high-availability inference architectures.
Optimize:
- Token throughput
- Response latency
- Cost efficiency
- Inference performance
Implement:
- Model routing
- Load balancing
- Caching
- Fallback strategies
Support batch and real-time inference.
Develop reusable deployment templates for different model families.

LLMOps / ModelOps / AgentOps

Build operational practices for managing models and agents throughout their lifecycle.
Implement observability for:
- Prompts
- Retrieval
- Model responses
- Latency
- Cost
- Failures
Develop evaluation pipelines for regression testing and continuous quality improvement.
Monitor:
- Model drift
- Response quality
- Hallucination indicators
- Safety risks
Support CI/CD and release management for:
- Prompts
- Models
- Agents
- Retrieval pipelines
Build dashboards and metrics for AI quality, reliability, adoption, and operational readiness.

AI Evaluation & Benchmarking

Define and implement LLM evaluation frameworks.
Measure:
- Accuracy
- Groundedness
- Relevance
- Hallucination rate
- Toxicity risk
- Safety compliance
- Task completion
- User satisfaction
Build automated test suites for prompts, agents, tools, and RAG pipelines.
Benchmark models across enterprise use cases.
Compare cloud, open-source, and on-prem models based on performance, cost, quality, and risk.
Establish quality gates for production AI releases.

Responsible AI, Security & Governance

Implement Responsible AI controls in LLM applications and agent workflows.
Develop guardrails for:
- Safe output
- Tool usage
- Data access
- Enterprise policy compliance
Support:
- Model risk management
- Auditability
- Transparency
- Traceability
Ensure sensitive data is handled according to security and privacy requirements.
Collaborate with Security, Enterprise Architecture, Risk, and Compliance teams.
Support model and agent approval and production-readiness governance.

Preferred / Additional Experience

Experience deploying open-source models such as:
- Llama
- Mistral
- Mixtral
- Phi
- Gemma
- Qwen
- DeepSeek
- Granite
- Falcon
- Domain-specific models
Experience with GPU infrastructure such as:
- NVIDIA H100
- H200
- B200
- B300
- A100
- L40S
- GH200
- AMD MI300X
Experience with:
- Private AI
- Hybrid AI
- Air-gapped AI environments
Experience in regulated industries such as:
- Healthcare
- Financial Services
- Insurance
Experience building:
- Enterprise copilots
- AI assistants
- Agent platforms
Experience with:
- MCP
- Tool registries
- Agent runtimes
- Enterprise integration patterns
Experience with Responsible AI, model governance, model risk management, and AI compliance.
Experience optimizing AI workloads for:
- Cost
- Performance
- Latency
- Security

Role / Skills Summary

Role: LLM Engineer
Essential Skill: LLM
Primary Skill: AI and Automation
Experience: 8-10+ years
Work Location: Remote USA
Duration: 1 year
Core Technologies: LLM, SLM, RAG, Generative AI, Agentic AI, Python, Kubernetes, MLflow, Hugging Face, PyTorch, vLLM, KServe, Docker, Vector Databases

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