Hire AI engineers who turn ideas into useful, reliable production systems.
Applied AI engineering bridging generative models, vector search, data pipelines, and scalable enterprise software.
Find suitable AI and machine learning engineering talent to help design, build, integrate, and evaluate AI-powered application capabilities. We specialize in Retrieval-Augmented Generation (RAG), embeddings, structured LLM extraction, and model evaluation.
Moving generative AI from prototype to production reliability
Anyone can build a quick chatbot demo with an API key. Building production AI requires solving hallucinations, enforcing strict output schemas, handling token costs, protecting sensitive enterprise data, and maintaining sub-second latency.
Our AI engineers build practical, grounded AI capabilities: hybrid vector search, context window optimization, automated evaluation harnesses, and human-in-the-loop review workflows.
Applied AI Engineering Capabilities
Specialized capabilities across RAG architectures, model fine-tuning, and production evaluation.
Retrieval-Augmented Generation (RAG)
Building document ingestion pipelines, semantic chunking, vector indexing, and hybrid reranking for accurate retrieval.
Structured Extraction & Agents
Extracting structured data from unstructured PDFs, emails, and images using function calling and strict Zod schemas.
Model Evaluation & Benchmarking
Authoring automated test harnesses (Ragas, TruLens) to measure answer relevance, faithfulness, and latency.
AI Product Integration
Connecting LLM capabilities to your existing web and mobile applications with streaming UI responses.
Privacy-First Architecture and Enterprise Guardrails
Enterprise data must remain private. Our AI engineers ensure your customer data is never used to train public models, implementing PII redaction, role-based document access controls, and zero-retention API endpoints.
What our applied ai & llm engineer specialists execute daily
Concrete day-to-day contributions aligned with your product backlog and release standards.
AI Pipeline Architecture
Designing document processing, vector indexing, and context window orchestration.
Prompt Engineering & Schema Enforcement
Writing optimized prompts with deterministic JSON outputs and few-shot examples.
Model Evaluation & Quality Monitoring
Tracking retrieval recall, generation accuracy, and hallucination rates in staging.
Application Integration
Building streaming API endpoints and integrating AI responses into React web interfaces.
Collaboration with Product and Engineering Teams
AI engineers collaborate with product managers to define realistic AI boundaries and with backend developers to integrate endpoints into existing software architectures.
Analyzing data quality and task complexity before committing to an AI architecture.
Ensuring all third-party model API calls comply with HIPAA, SOC 2, and GDPR requirements.
Providing executive visibility into model accuracy, token costs, and user satisfaction scores.
Designing interfaces with user feedback loops (thumbs up/down) to continuously improve results.
Interactive RAG Knowledge Query & Citation Assistant
Simulate an enterprise knowledge query: inspect document chunk retrieval, vector similarity scores, and grounded answer synthesis with clickable source citations.
Verified Technical Stack & Tooling
We only deploy talent with hands-on, production experience in the specific technologies your team relies upon.
How we evaluate and select applied ai & llm engineer specialists
Every specialist undergoes a 4-stage technical vetting process before presenting candidate profiles to you.
RAG Architecture & Retrieval Strategy
Evaluating chunking strategies, embeddings choice, and hybrid search trade-offs.
Live Prompt & Schema Coding
Building a structured extraction pipeline that parses messy text into strict JSON.
AI Quality & Hallucination Mitigation
Diagnosing model failure cases and designing automated evaluation metrics.
Client Technical Interview
Direct conversation with your technical lead to evaluate project feasibility and fit.
From briefing to merged production pull requests
A disciplined, phased onboarding roadmap designed to integrate specialists into your active sprint board in days, not months.
Data Access & Baseline Setup
First Retrieval Prototype
API Integration & Streaming
Production Hardening
Choose your working arrangement
Tailored staffing arrangements designed to fit your roadmap velocity, budget, and management structure.
Dedicated Applied AI Engineer
AI Architecture Specialist
AI Feature Sprint Squad
Building an Enterprise Policy & Compliance Knowledge Assistant
Allow 5,000 corporate users to query 200,000 pages of legal contracts with verifiable source page citations in under 2 seconds.
2 Dedicated Senior AI Engineers (Python, pgvector, LangChain, Gemini API).
Shipped production RAG pipeline with 99.4% answer faithfulness and zero hallucinations; reduced internal contract research time by 82%.
Ready to build practical, reliable AI capabilities into your product?
Review vetted AI engineers with proven experience in RAG, vector search, and structured LLM extraction.
Frequently Asked Questions About Hiring APPLIED AI & LLM ENGINEER
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Let’s discuss the AI engineering talent your project needs.
Speak with our AI practice lead about your data assets, technical feasibility, and development timeline.
