Hire an AI Engineer. Applied AI Agents. Governed LLM Workflows. Deterministic AST Intelligence. - Aleksandar Kuzmanovic offers many high-quality services. Are you looking to hire an applied AI engineer who builds robust, production-grade intelligence systems rather than fragile chatbot wrappers? My name is Aleksandar Kuzmanovic. I design and build production AI agents, AST-governed code analysis engines, and deterministic Text-to-SQL pipelines. Key stats: <800ms P95 latency, 99.4% Benchmark accuracy, 0 Breaches SQLi resistance, <45s PR turnaround. Technologies: Python, TypeScript, Next.js 16, FastAPI, AWS Bedrock, PostgreSQL, Redis & BullMQ, Docker, Cube.js, DynamoDB, Amazon S3, AST Parsers, GitHub App API, TailwindCSS. Contact: aleksandarkuzmanovic02@gmail.com.
Hire an AI Engineer.
Applied AI Agents. Governed LLM Workflows. Deterministic AST Intelligence.
Are you looking to hire an applied AI engineer who builds robust, production-grade intelligence systems rather than fragile chatbot wrappers? My name is Aleksandar Kuzmanovic. I design and build production AI agents, AST-governed code analysis engines, and deterministic Text-to-SQL pipelines.
Enterprise AI fails when models are trusted with unconstrained execution, unrestricted SQL generation, or blind API access. My engineering methodology removes the LLM from direct execution — using models exclusively for intent understanding, while delegating policy enforcement, security guardrails, and execution to deterministic AST compilers, worker queues, and tamper-evident audit logs.
With 12+ years of programming and deep cloud systems experience, I have architected distributed AI platforms: from multi-provider Git review agents processing asynchronous PR inspections in under 45 seconds to governed enterprise Text-to-SQL intelligence engines achieving 99.4% accuracy with zero SQL injection vectors.
Whether you need to automate engineering workflows, build proprietary internal intelligence agents, or deploy low-latency LLM orchestration pipelines on AWS, I deliver enterprise-grade architecture engineered for verifiable business impact.
Are you looking to hire an applied AI engineer who builds robust, production-grade intelligence systems rather than fragile chatbot wrappers? My name is Aleksandar Kuzmanovic. I design and build production AI agents, AST-governed code analysis engines, and deterministic Text-to-SQL pipelines.
Services & deliverables.
End-to-end.
Autonomous AI Agents & Webhook Automation
Production-ready agents connected to GitHub, GitLab, Slack, or internal webhooks with cryptographic verification and background task queuing.
Governed Text-to-SQL & Analytics Engines
Secure natural-language business intelligence layers with deterministic semantic compilers, row-level isolation, and interactive charting.
Automated Code Review & Security Auditing Pipelines
Enterprise PR review systems with dual-depth analysis (maintainability vs. deep asymptotic Big-O algorithmic inspection).
Two-Tier Caching & Latency Optimization
Redis-backed commit SHA and embedding caches that cut inference latency to <50ms and reduce token spend by up to 80%.
Developer Tooling & Native Extensions
Native VS Code extensions, zero-dependency CLI developer tools, and back-office management dashboards.
Enterprise Architecture Audits & GEO Integration
Security auditing of LLM prompt boundaries, data sanitization, and structured data optimization for generative AI search engines.
Why hire me as your AI engineer?
Deterministic AST Guardrails & Security
I never trust raw LLM outputs in production. I build abstract syntax tree (AST) policy engines that validate, sanitize, and restrict generated code and queries prior to database execution, eliminating prompt injection and data leaks.
Production Agent Workflows & Webhooks
From cryptographic GitHub App webhooks (X-Hub-Signature-256) to multi-stage CI/CD checks, I build asynchronous agent ecosystems with BullMQ, Redis caching, and PostgreSQL concurrency locks that scale without timeouts.
Governed Text-to-SQL & Enterprise Analytics
I architect governed semantic querying layers (Cube.js, PostgreSQL, AWS Bedrock) that translate plain-English executive questions into strictly typed, row-level isolated relational queries with tamper-evident SHA-256 audit logs.
Multi-Provider Developer Tooling
I integrate AI intelligence directly into engineering workflows: native VS Code extensions with inline Problems diagnostics, zero-dependency CLI developer tools, and web dashboards with fail-closed repository permissions.
Semantic Caching & Cost Optimization
AI API costs scale rapidly without intelligent architecture. I engineer two-tier Redis caching (commit SHA and vector similarity) that delivers instant HTTP 202 hits at cost 0 for identical inputs.
Complete Generative Engine Optimization (GEO)
I transform application data and knowledge bases into semantic Schema.org entity graphs, machine-readable summaries, and citation-grounded outputs optimized for Google AI Overviews, Perplexity, and ChatGPT Search.
My process.
Simple. Transparent.
Use-Case & Data Governance Discovery
We analyze your business workflows, data privacy requirements, database schemas, and accuracy criteria to define the optimal AI architecture.
Deterministic Pipeline & AST Design
I architect the intent compilation layer, prompt pruning vectors, AST security allowlists, and database access controls.
Asynchronous Worker & Queue Engineering
Implementation with BullMQ/Redis, background job idempotency, ephemeral credential vaults, and multi-tenant isolation.
Benchmark Testing & Security Hardening
Automated test suites evaluate accuracy on multi-table schemas and subject the pipeline to 150+ adversarial SQL injection and prompt extraction attacks.
Production Deployment & Observability
Deployment with Docker on AWS or dedicated Linux infrastructure with hash-chained audit logging and real-time latency monitoring.
Technology Stack
The discipline of applied AI engineering.
In production engineering, AI models are components, not silver bullets. The difference between an experimental prototype and an enterprise-grade AI system lies entirely in the software engineering around the model: deterministic compilers, strict type systems, abstract syntax tree (AST) policy governors, and asynchronous queue orchestration.
When building DataPulse, we demonstrated that achieving 99.4% execution accuracy across enterprise relational databases requires removing the LLM from direct SQL generation. By forcing the model to produce structured intermediate representations and delegating SQL compilation to a deterministic AST engine, cross-tenant data leaks and injection attacks are mathematically eliminated.
Similarly, on the AI Code Reviewer platform, processing thousands of pull requests demands asynchronous decoupling. By utilizing BullMQ queues on Redis, two-tier commit SHA caching, and ephemeral token vaults with 15-minute TTLs, systems achieve sub-45-second turnarounds with zero timeouts and zero persisted credentials.
This is the engineering rigor I bring to every engagement. Whether your organization is modernizing internal business intelligence or launching an autonomous agent ecosystem, I build systems engineered for determinism, security, and measurable ROI.
Common questions.
Clear answers.
- Why hire an applied AI engineer instead of using an out-of-the-box LLM API?
- Out-of-the-box LLM APIs provide raw completions, but they do not solve schema hallucination, cross-tenant security isolation, deterministic execution, or latency bottlenecks. An applied AI engineer architects the end-to-end governed infrastructure: data retrieval, AST validation, queue orchestration, caching, and CI/CD integration.
- How do you prevent LLM hallucinations and SQL injection in database agents?
- Through deterministic compilation and AST policy enforcement. The LLM produces a structured intermediate intent (such as a Semantic Model Query); a deterministic compiler generates the SQL; an AST policy visitor injects row-level security (RLS) directly into the parse tree; and execution is constrained to read-only connections with automated query timeouts.
- Can you integrate AI agents into our existing GitHub or GitLab workflow?
- Yes. I specialize in native GitHub Apps and GitLab webhooks with cryptographic signature verification. Reviews run asynchronously via background workers, post structured markdown comments, and trigger status checks that gate pull request merges based on severity thresholds.
- What cloud platforms and models do you support?
- I work across AWS (Bedrock, ECS, Lambda, DynamoDB, S3), GCP, and independent infrastructure using models from Anthropic (Claude 3.5 Sonnet), OpenAI (GPT-4o), and open-weight models via Ollama and vLLM.
- How do you measure and verify AI performance in production?
- Every agent includes cryptographic audit logging (SHA-256), tracking prompt token cost, P50/P95 response latency, execution accuracy against curated benchmark test sets, and error rates with automated alerts.
Ready to start?
Send a message and I'll respond within 24 hours with a clear plan for your project.