Interested in LLM Engineering & Retrieval?
Share goals, constraints & objectives - I'll follow up quickly with clarifications or an initial approach.
Service
Engagement Ready
LLM Engineering & Retrieval
Architecture & implementation of retrieval workflows, model selection, evaluation harnesses, guardrails & deployment pathways for AI features.
Snapshot
- Focused on measurable reliability & iteration velocity
- Structured failure surfaces & escalation paths
- Composable orchestration patterns
Why It Exists
Distills patterns from real R&D & production experimentation into a repeatable engagement surface for faster outcomes.
What You'll Get
- Vector / hybrid retrieval design
- Prompt strategy & evaluation harness
- Guardrails / safety & fallback flows
- Model selection & cost/performance analysis
- Fine-tuning / adapters (when justified)
Why This Service Matters
LLM Engineering & Retrieval isn't just an offering - it's a lever for accelerating delivery, reducing waste, and creating compounding operational advantage. Engagements are lean, outcome-focused, and structured so you get clarity early, momentum quickly, and durable assets at handover.
Approach
Founder-level thinking + senior execution. No bloated layers - direct collaboration & async progress.
Delivery
Iterative build cycles with visible increments. Scope adapts with validated feedback, not guesswork.
Outcomes
Optimized for measurable impact: time saved, cost reduced, capability gained, or velocity increased.
Hands-On Senior Delivery
Direct access to the builder. Strategy, architecture & implementation aligned - no translation loss.
No-Fluff Process
Lightweight artifacts, fast iteration, asynchronous updates. Decisions grounded in measurable impact.
Compounding Assets
Code, automation & architecture you can extend - documented, observable, production-ready.
Typical Capability Areas
Architecture & Foundations
Initial platform setup, domain modeling, auth, environments, observability hooks.
Backend & APIs
Django services, REST/GraphQL endpoints, background jobs, integration layers.
Automation & AI Assist
Task orchestration, LLM-powered enrichment, document workflows & agentic tooling.
Frontend & UX Flows
Responsive interfaces, HTMX progressive enhancement, dashboard & admin surfaces.
Data & Performance
Query tuning, caching, async pipelines, metrics instrumentation.
Delivery & Continuity
CI/CD, deployment hardening, post-launch iteration & technical debt surfacing.
Core Stack & Tooling
Django / Python
PostgreSQL / Redis
LangChain / LLM Ops
HTMX / Tailwind
Docker / CI/CD
Observability & Metrics
Ready to Explore LLM Engineering & Retrieval?
Share current state, constraints & desired outcomes. I’ll respond with clarifications or an initial path within 24 hours.
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