Multi-Language RAG Pipeline & Cospas-Sarsat Search & Rescue Technical Assistant

Architected and engineered a production-grade, containerized Retrieval-Augmented Generation (RAG) pipeline designed for emergency search and rescue console operations. Built a cross-lingual system that enables operators to query complex technical manuals, standards, and beacon specification sheets in multiple languages (English, French, and Russian) while ensuring semantic retrieval alignment from English source documentation. The core platform features strict prompt engineering overrides to eliminate LLM hallucinations, ensuring all technical answers are deterministically tied to verified documentation fragments for high-stakes operational environments.

Metrics at a Glance:

Key Architectural Achievements:

• Cross-Lingual Retrieval Mechanism: Integrated a multilingual embedding space alignment model that maps non-English semantic search expressions directly against English text chunks, enabling seamless English, French, and Russian interaction surfaces.
• Native Containerized Ingestion Bootstrapping: Engineered an isolated Linux-native document ingestion workflow that dynamically scans local PDF directory structures and safely parses text into optimized vector chunks without triggering OS deserialization or thread crashes.
• Strict Contextual Prompt Enforcement: Formulated localized prompt boundaries using specialized Llama 3.1 tokens (<|start_header_id|>) to isolate execution instructions, ensuring the pipeline completes queries cleanly using only source information.
• Visual Audit Trace Panel: Designed a maritime-themed user interface containing split verification components for telemetry logs and expandable reference material buckets, allowing operators to audit file lineages, page positions, and raw source text.
• Hardened Non-Root Container Footprint: Built a robust container framework tailored for strict cloud infrastructure hosting, packaging required C++ compilation dependencies and enforcing non-root UID 1000 file write permissions for persistent disk safety.

Local RAG Observability System & AI Technical Assistant

Architected and engineered a production-grade, CPU-optimized Local Retrieval-Augmented Generation (RAG) pipeline designed to process massive text corpora (10M Document Modular Scale) without data leakage risks. Built a secure, air-gapped system running entirely inside isolated Docker containers using open-weights models to parse local PDF repositories. The core platform wraps every processing block—from dense vector ingestion to statistical cross-encoder reranking—in strict OpenTelemetry spans, streaming live telemetry metrics into a local Arize Phoenix engine to deterministically catch hardware latencies and factual hallucinations on the edge.

Metrics at a Glance:

Key Architectural Achievements:

• Multi-Span OpenTelemetry Instrumentation: Integrated full native OTel tracing using openinference-instrumentation-langchain to auto-intercept internal execution flows and expose custom child spans (Intent_Router, Retriever, FlashRank_Reranker) for step-by-step performance audits.
• Two-Stage Optimized Retrieval: Developed an efficient retrieval matrix combining cosine similarity indexing inside Qdrant with a local FlashRank statistical cross-encoder, compressing the top 10 raw vector hits down to the top 3 highest-quality passages.
• Hardware-Aware Ingestion Queue: Engineered a sliding chunk array queue mechanism (BATCH_SIZE_CHUNKS = 1000) that offloads structural vector lookup tables directly to physical SSD storage, enabling massive scale processing without memory spikes.
• CPU-Driven Micro-Inference Guardrails: Formulated a localized, deterministic string-matching evaluation script that filters high-frequency structural stop-words to instantly identify context anomalies and eliminate false-positive hallucination overlaps on consumer-grade hardware.
• NoOpTracer Error Fallback Layer: Configured a resilient telemetry routing engine that automatically switches to a silent No-Operation fallback trace mode if Docker metrics endpoints crash, preventing script failure and keeping user prompts uninterrupted.

Asymmetric Intent-Driven GenAI Workload Routing Engine (Cost Optimization POC)

Architected and engineered an asymmetric, intent-driven architectural routing layer designed to maximize GenAI cost optimization and runtime latency efficiency. Moving away from rigid, single-model frameworks, the platform intercepts incoming prompts and dynamically routes them across three discrete computational tiers using a local vector embedding classification layer. Features a self-healing error escalation mechanism that automatically catches transient network or provider faults at Tier 1 or 2, wrapping payloads with diagnostic telemetry and seamlessly cascading execution to Tier 3 frontier networks to maintain 100% downstream uptime.

Metrics at a Glance:

Key Architectural Achievements:

• Asymmetric Multi-Tier Policy Mapping: Implemented an intent-driven router that automatically offsets expensive frontier token usage by offloading low-complexity tasks (JSON extraction, formatting, drafting) to high-speed, open-weight models.
• Local Multidimensional Semantic Clustering: Embedded a lightweight, high-performance sentence transformer to vectorize incoming user prompts and compute mathematical cosine similarity scores against preset category anchors instantly on local hardware.
• Automated Self-Healing Escalation Protocol: Engineered a robust asynchronous fallback loop that traps downstream provider rate limits or system exceptions, automatically escalating compromised workloads safely to frontier instances without user intervention.
• Cross-Vendor Dependency Conflict Resolution: Successfully debugged and mitigated breaking asynchronous multi-vendor network initialization bugs by pinning strict cross-compatible HTTPX client layers to prevent keyword-argument runtime crashes.
• Real-Time Performance Telemetry Dashboard: Developed an independent graphical monitoring console rendering instant telemetry indicators including cosine match confidence percentages, assigned operational tiers, and real-time execution network latency metrics.