The demand for engineers who can build production-grade AI systems, not just run notebook experiments, far outstrips supply. There is a substantial gap between an engineer who has used ChatGPT and one who has deployed a RAG pipeline to many thousands of users.
The same gap separates a casual experimenter from someone who has implemented a fine-tuned model with feedback loops, or built an autonomous agent with reliable tool-calling.
Our AI engineering network comprises practitioners with verifiable production experience. Every candidate is assessed on architecture, implementation quality, and reasoning about failure modes, not just framework familiarity.
LLM and ML Track Assessment
Production Experience Required
Shortlist Turnaround
Code and IP Protection
Specialists in OpenAI, Anthropic, and open-source LLM APIs. Build RAG pipelines, prompt engineering systems, and context-management strategies for reliable production behavior.
Vector database design across Pinecone, Weaviate, and pgvector. Build retrieval architecture, hybrid search, and re-ranking systems that deliver relevant results at scale.
Training pipeline design, feature engineering, model evaluation, and deployment to serving infrastructure. Bridge data science and production engineering.
Multi-step agent systems using LangChain, LlamaIndex, and AutoGen. Implement tool use, memory, and planning loops that automate real workflows reliably.
Supervised fine-tuning, LoRA, QLoRA, DPO, and RLHF. Adapt foundation models to domain-specific needs without rebuilding capability from scratch.
ML pipeline automation, model versioning, A/B testing infrastructure, and drift monitoring. Keep models reliable and observable after they leave the lab.
Our vetting process for AI engineers is more rigorous than a standard technical screen because the domain is harder to assess from a CV. We test for production thinking, not just tool familiarity.
Knowledge base Q&A system for a large enterprise workforce. Delivered measurable reduction in support ticket volume and faster resolution for repetitive queries.
Real-time personalization model serving high-volume daily recommendations. Tuned for low-latency response under concurrent production load.
Automated extraction and classification of high-volume legal documents. Reduced manual review effort while improving classification consistency.
Semantic search layer replacing keyword search on a content-heavy platform. Materially improved search-to-conversion rate and zero-result recovery.
Multi-agent workflow automating a multi-step operational process. Cut processing time from hours to minutes while maintaining audit trails.
Fine-tuned foundation model for industry-specific terminology and reasoning. Improved task accuracy versus general-purpose model baselines.
Our vetting process focuses on architecture design and failure-mode reasoning. We test the ability to handle hallucinations, latency at scale, and vector database optimization. Every engineer we place has a verifiable history of deploying live systems, not just running local notebooks.
Yes. We provide specialized machine learning engineers who focus on the operations side of AI, including model versioning, A/B testing infrastructure, and drift monitoring. Whether you are using PyTorch, TensorFlow, or cloud-native services like AWS SageMaker and Vertex AI, we match you with talent that understands how to automate the full ML lifecycle.
Generalist developers often struggle with the nuances of non-deterministic outputs. When you hire LLM engineers from our network, you get specialists in context window management, prompt chaining, and advanced RAG architectures. They understand the trade-offs between models like GPT, Claude, and Llama, and can implement reliable tool-calling and memory loops for autonomous agents.
We maintain an active, pre-vetted network specifically to solve the AI talent shortage. Once you submit your brief, we deliver a shortlist of top candidates within days. Because our engineers are already screened for technical and soft skills, they can typically be onboarded and contributing to your codebase within the first working week.
You retain full ownership of all intellectual property. This includes application code, custom-built RAG pipelines, fine-tuned model weights, and proprietary datasets developed during the engagement. All work is covered by standard NDA and IP assignment agreements, so your competitive advantage is fully protected.
Yes, that is one of the most common engagement patterns. Our AI engineers integrate into your existing structure, attend your standups, and use your tools. They typically pair with internal data scientists to take experimental work to production, while your team retains domain knowledge and roadmap ownership.
Since 1999, SunTec has been a trusted business process and software engineering partner for technology companies worldwide. Our team of over 1,500 full-time employees delivers innovative services every day to help our clients save time and money.
