●Technology Expertise

AI/ML Integration
Development.

Expert AI/ML Integration development with deep production experience. From architecture decisions to performance optimization, I help teams build systems that scale.

ai integration developerllm engineerrag developerai/ml consultantchatbot developervector database specialist

●Expertise Level

Building AI-integrated systems since GPT-3 (2020). Deep experience with LLM prompt engineering, RAG architectures, and embedding-based search. Trained custom models using LoRA/QLoRA fine-tuning, deployed inference servers handling 1M+ daily requests, and built evaluation frameworks measuring AI quality at scale.

●When to Use AI/ML Integration

Adding intelligent features to existing applications—search enhancement, content generation, anomaly detection

Building RAG (Retrieval-Augmented Generation) systems that combine LLMs with proprietary knowledge bases

Implementing semantic search using vector embeddings when keyword matching fails for natural language queries

Automating workflows with AI agents that can reason, plan, and execute multi-step tasks

Processing unstructured data (documents, images, audio) at scale using purpose-built ML models

Creating conversational interfaces where context understanding and response quality matter

Enhancing user experience with personalization, recommendations, or predictive features

●Best Practices

Use structured outputs (function calling, JSON mode) instead of parsing free-form LLM text—eliminates regex failures

Implement tiered model routing: fast/cheap models for classification, expensive models for complex reasoning

Cache embeddings and LLM responses aggressively—identical inputs should hit cache, not API

Build evaluation pipelines before production—measure accuracy, latency, and cost on representative datasets

Use streaming responses for chat interfaces—perceived latency drops dramatically with token-by-token display

Implement fallback chains: primary model fails → retry with higher temperature → fallback model → human escalation

Version prompts like code: git-tracked templates with variable injection, not hardcoded strings

●Common Pitfalls to Avoid

Using GPT-4 for everything—smaller models (GPT-3.5, Claude Haiku, Mistral) are 10-100x cheaper for simple tasks

Not implementing proper prompt versioning—prompt changes can break production without tracking

Ignoring embedding model choice—text-embedding-3-small vs ada-002 have different dimension/quality tradeoffs

Building RAG without hybrid search—combine vector similarity with BM25 keyword matching for better recall

Not chunking documents properly—512-1024 tokens with 50-100 token overlap prevents context splitting

Forgetting that LLM outputs are non-deterministic—use seed parameter and temperature=0 for reproducibility

Underestimating inference costs—a viral feature using GPT-4 can cost $10K/day without rate limiting

●Ideal Project Types

Intelligent search and knowledge retrieval

Content generation and summarization

Conversational AI and chatbots

Document processing and extraction

AI-powered automation workflows

Recommendation and personalization systems

●Complementary Technologies

OpenAI API or Anthropic Claude (LLM providers)

Pinecone, Qdrant, or pgvector (vector databases)

LangChain or LlamaIndex (LLM orchestration)

Hugging Face Transformers (open models)

Redis (caching embeddings and responses)

Celery/Redis (async inference queues)

●Real-World Example

Case Study

PenQWEN demonstrates advanced AI/ML integration. The project required a custom cybersecurity LLM that could understand domain terminology, execute tool calls, and maintain operational security awareness. I implemented a two-stage fine-tuning pipeline: first, continued pre-training on 12GB of curated security corpus (CVE databases, penetration testing guides, threat intelligence reports) to inject domain knowledge. Second, supervised fine-tuning for agentic behavior—the model learned to call tools (nmap, metasploit modules) and reason about OPSEC implications. The training used Qwen2.5-7B as the base with LoRA adapters (rank=64, alpha=128), resulting in 3.6GB of trainable weights. For deployment, I built a FastAPI inference server with dynamic LoRA loading—the base model stays in VRAM while task-specific adapters swap in 200ms. The RAG component uses Qdrant for retrieval over exploit databases, with hybrid search combining dense embeddings (bge-large-en-v1.5) and sparse BM25. The result: a specialized model that outperforms GPT-4 on cybersecurity tasks at 1/10th the inference cost.

●Ready to Build?

Let's discuss your
AI/ML Integration project.

Whether you're starting fresh, migrating an existing system, or need architectural guidance, I can help you build with AI/ML Integration the right way.

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