You will get LLM Evaluation Framework for Production AI Systems
Project details
I build evaluation infrastructure that tells you whether your
AI system actually works — before it fails in production.
What you get:
LLM-as-a-Judge harness with golden datasets and multi-metric scoring
RAGAS integration: faithfulness, relevancy, factuality, context recall
Regression logging with score delta tracking across runs
Prompt injection detection (5 taxonomies) and PII redaction guardrails
HITL safety gates and output moderation
OpenTelemetry instrumentation + Jaeger trace visibility
Built for teams who need confidence in their LLM outputs, not just vibes.
AI system actually works — before it fails in production.
What you get:
LLM-as-a-Judge harness with golden datasets and multi-metric scoring
RAGAS integration: faithfulness, relevancy, factuality, context recall
Regression logging with score delta tracking across runs
Prompt injection detection (5 taxonomies) and PII redaction guardrails
HITL safety gates and output moderation
OpenTelemetry instrumentation + Jaeger trace visibility
Built for teams who need confidence in their LLM outputs, not just vibes.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AIOps, Anomaly Detection, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment AnalysisAI Development Language
PythonAI Tools
GitHub Copilot, PyTorchAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$1,500
|
Standard
$3,500
|
Advanced
$5,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 1 | 2 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | |
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | ||
Model Deployment | - | - | |
Model Documentation | |||
Model Monitoring | - | ||
Model Testing & Optimization | - | ||
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | - | ||
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$300 - $800
Additional Revision
+$200
Golden Dataset Expansion
(+ 2 Days)
+$400
Observability Integration
(+ 3 Days)
+$500Frequently asked questions
About Sergiu
AI Engineer | LLM & Agentic Systems | GraphRAG | Neo4j | MCP | A2A
Timisoara, Romania - 5:45 am local time
I design and deploy reliable LLM-powered applications with deterministic orchestration, persistent memory and rigorous evaluation. My core expertise is building multi-agent systems using LangGraph and AutoGen, with strong focus on stateful orchestration, tool integration and reproducibility.
My flagship project is a knowledge graph platform on Neo4j with contradiction detection, document authority hierarchies, alias deduplication, ontology enforcement and graph health monitoring — treating graph integrity as a first-class engineering concern. Measured, not claimed: 0.940 RAGAS faithfulness on a golden set, 2.2s p95 retrieval latency, 364 passing tests. I also build end-to-end RAG pipelines with hybrid retrieval and grounded generation, and designed a reproducible benchmarking framework evaluating recall–latency–throughput trade-offs across vector search engines (Qdrant, Elasticsearch, pgvector, Redis).
With 10+ years of enterprise engineering experience operating 10M+ user platforms at 99.9% uptime (Alcatel-Lucent), I bring the same discipline to AI systems: observability, CI/CD and cloud-native deployment. All AI engineering work is self-directed and independently deployed — built to enterprise standards from day one.
Core stack: Gemini • GPT • Claude • Llama • LangGraph • AutoGen • CrewAI • Google ADK • LangChain • LlamaIndex • PydanticAI • DSPy • Haystack • Semantic Kernel • FastAPI • gRPC • Python • PyTorch • TensorFlow • Scikit-learn • React • TypeScript • PostgreSQL • pgvector • Neo4j • Qdrant • Milvus • Weaviate • Pinecone • FAISS • OpenSearch • Docker • Kubernetes • Google Cloud Run • Vertex AI • AWS SageMaker • Redis • RabbitMQ • OpenTelemetry • Langfuse • LangSmith • Prometheus • Grafana • Jaeger • RAGAS • LLM-as-a-Judge • MCP • A2A • HITL • Deepgram • sentence-transformers • TimescaleDB • Alembic • Claude Code • Cursor • RRF • BM25 • Cross-encoder reranking • OWL • SPARQL • Cypher • JWT • OIDC • ABAC • WebSocket • gRPC-Web • Envoy • GitHub Actions • CI/CD
Steps for completing your project
After purchasing the project, send requirements so Sergiu can start the project.
Delivery time starts when Sergiu receives requirements from you.
Sergiu works on your project following the steps below.
Revisions may occur after the delivery date.
Intake & Scope Definition
Share your LLM stack, existing prompts, and failure modes you care about. I define evaluation scope, golden dataset structure, and metric targets.
Harness Build
LLM-as-a-Judge pipeline, RAGAS integration, regression logging, prompt injection detection, PII redaction, and HITL safety gates implemented against your system.