Prompt Engineer
Navigating the complexities of a prompt engineer job in the modern enterprise requires more than basic linguistic intuition; it demands a deep understanding of stochastic parity, latent space navigation, and the systemic orchestration of Large Language Models. As a premier LLM prompt specialist, we bridge the gap between raw model capabilities and deterministic business logic, ensuring that your prompt engineering career or internal AI initiatives deliver production-grade reliability and unprecedented operational efficiency.
Prompt Engineer
Bridge the gap between human intent and machine execution. Join the elite team defining the next generation of enterprise AI orchestration at Sabalynx.
Architecting the Latent Space
At Sabalynx, Prompt Engineering is not a hobby—it is a rigorous discipline of linguistics, logic, and software engineering. We are seeking a Prompt Engineer who treats natural language as a high-level programming language to steer Large Language Models (LLMs) with surgical precision.
You will be responsible for the “Brain” of our enterprise solutions. You will work across the stack, from fine-tuning system instructions for autonomous agents to optimizing RAG pipelines that process petabytes of proprietary client data. This role requires a practitioner who understands the stochastic nature of transformers and seeks to impose deterministic reliability upon them.
Quick Specs
Key Responsibilities
Advanced Prompt Orchestration
Design, develop, and test sophisticated multi-turn prompts using Chain-of-Thought (CoT), Tree-of-Thought, and ReAct patterns to solve complex reasoning tasks.
RAG Optimization
Architect Retrieval-Augmented Generation workflows, focusing on query expansion, decomposition, and reranking to minimize hallucinations and maximize context relevance.
Evaluation Frameworks
Build and maintain rigorous “LLM-as-a-judge” evaluation pipelines to quantify performance, using metrics like faithfulness, answer relevance, and context precision.
AI Security & Guardrails
Implement adversarial testing (red-teaming) to identify prompt injection vulnerabilities and deploy robust system-level guardrails for enterprise compliance.
Inference Cost Management
Optimize token utilization and context window efficiency to reduce operational overhead without compromising model intelligence or response latency.
Cross-Functional Collaboration
Partner with Backend Developers and MLOps Engineers to integrate prompts into production APIs, ensuring seamless transitions from playground to deployment.
Dataset Curation for Fine-Tuning
Identify performance gaps where prompting fails and collaborate with data teams to curate synthetic or human-annotated datasets for model distillation or fine-tuning.
Agentic Workflow Design
Engineer state-machine-based AI agents that can utilize tools, navigate loops, and execute autonomous tasks with high reliability and goal-persistence.
Required Qualifications
- Bachelor’s or Master’s in Computer Science, Linguistics, Cognitive Science, or a related technical field.
- 3+ years of experience working with LLMs (OpenAI, Anthropic, Meta, or Open Source models).
- Proficiency in Python and familiarity with AI frameworks (LangChain, LlamaIndex, Haystack).
- Demonstrated expertise in prompt engineering techniques (Few-shot, CoT, System Messages).
- Deep understanding of Vector Databases (Pinecone, Weaviate, Milvus) and embedding models.
- Strong analytical skills—ability to debug non-deterministic outputs and edge cases.
Nice-to-Have Skills
- Experience with Multi-modal models (Vision, Audio, Video).
- Background in NLP (Named Entity Recognition, Sentiment Analysis, Dependency Parsing).
- Familiarity with fine-tuning techniques (LoRA, QLoRA, P-Tuning).
- Contribution to open-source AI projects or research publications.
- Knowledge of frontend development (React) to build internal prototyping tools.
What We Offer You
Intellectual Sovereignty
We hire experts and get out of their way. You will have the autonomy to experiment with the latest models 48 hours after they drop.
High-Stakes Projects
Forget toy apps. You will build AI for global banks, healthcare networks, and logistics giants where accuracy is non-negotiable.
Competitive Package
Tier-1 salary, performance-based bonuses, and a dedicated budget for hardware and continuous AI education.
Radical Transparency
A flat hierarchy where the best prompt wins. We value technical truth over seniority or internal politics.
Ready to Code in
Plain English?
We are looking for the 0.1% of practitioners who see the future of software as a conversation. If you speak ‘Transformer’, we want to hear from you.
Architecting the Interface of Intelligence
Working as a Prompt Engineer at Sabalynx is not about “chatting” with models—it is about rigorous linguistic architecture, deterministic output control, and the orchestration of complex agentic workflows.
Multi-Model Orchestration
We are model-agnostic. You will build abstraction layers that route queries between GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, and specialized Llama-3 fine-tunes based on latency, cost, and semantic complexity.
Systemic Evaluation Frameworks
Our engineers don’t rely on “vibes.” You will implement automated evaluation pipelines using G-Eval, RAGAS, and custom LLM-as-a-judge architectures to quantify precision, recall, and hallucination rates in production.
Prompt Lifecycle Management (PLM)
Treating prompts as code is our standard. You will manage versioned prompt registries, implement A/B testing on system instructions, and optimize context window utilization via sophisticated RAG and cache-aware strategies.
Agentic Reasoning & Tool Use
Move beyond zero-shot prompting. We design multi-step reasoning chains (CoT, ReAct) that allow AI agents to interact with external APIs, SQL databases, and legacy ERP systems with surgical accuracy.
Rigorous by Design
We hire the top 0.1% of AI practitioners. Our process is designed to test architectural depth, prompt optimization skills, and the ability to solve non-deterministic problems.
Architectural Diagnostic
A 45-minute technical deep-dive with a Senior AI Architect. We bypass the basics and discuss tokenization nuances, embedding strategies, and how you’ve handled prompt injection and jailbreaking risks in enterprise environments.
The Prompt Optimization Sandbox
A live coding/prompting exercise. You’ll be given a failing RAG pipeline with high latency and low accuracy. Your task: optimize the system prompt and retrieval strategy to meet a specific KPI under a strict token budget.
Agentic Workflow Integration
Collaborative session with our Engineering Lead. We analyze how your prompts integrate with Python/TypeScript backends. We look for expertise in Function Calling, JSON-mode stability, and handling state in multi-turn agentic conversations.
Principal Alignment
Final conversation with the CTO or Head of AI. We discuss the future of the field—from Sparse Priming Representations to the shift toward DSPy and automated prompt optimization. We’re looking for visionary thinkers who anticipate the next architectural shift.
Typical time-to-offer: 10 business days.
Ready to Deploy Prompt Engineer?
The difference between a stochastic toy and a deterministic enterprise tool lies in the precision of the instruction set. We invite you to book a complimentary 45-minute discovery call with our lead AI architects. During this session, we will conduct a preliminary audit of your LLM interaction patterns, evaluate your chain-of-thought orchestration logic, and identify critical vectors for reducing token latency and hallucination rates in your production environment.