AI Prompt Engineering Lead (Agentic AI & Hiring Automation) - Remote job opportunity at Cynet Corp.

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Date2026-01-06T12:34:50.118Z bot
Cynet Corp AI Prompt Engineering Lead (Agentic AI & Hiring Automation) - Remote
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Location: Remote / Dehradun (Hybrid options available) Engagement Model: Part-time / Contractual Time Commitment: 8–10 Hours / Week Role Mandate We are soliciting applications for a Senior AI Prompt Engineering Lead to architect, govern, and optimize high-fidelity Large Language Model (LLM) systems. This role is positioned at the intersection of Agentic AI and Hiring Automation , requiring a sophisticated approach to building systems that recruit, evaluate, and interact with human talent autonomously. This is not a content generation role; it is a systems engineering role . You will be responsible for designing the cognitive architecture of our platform, utilizing frameworks such as LangChain and LangGraph to build deterministic, scalable, and reasoning-capable agents for production environments. Core Responsibilities 1. Advanced Prompt Architecture & Cognitive Modeling Strategic Design: Engineer production-grade prompt infrastructures for complex workflows, including candidate evaluation, resume parsing, interview automation, and autonomous stakeholder communication. Methodology Implementation: Deploy advanced prompting paradigms—including Chain-of-Thought (CoT), Tree-of-Thought, Self-Consistency, and Instruction Hierarchies—to ensure high-precision reasoning. Constraint Engineering: Architect robust guardrails and instruction-following protocols to maintain system safety, prevent jailbreaks, and ensure strict adherence to hiring rubrics. 2. Agentic AI & Workflow Orchestration System Construction: Build and manage stateful, multi-agent workflows using LangGraph and LangChain . Decision Logic: Design complex, multi-step decision trees that incorporate human-in-the-loop (HITL) checkpoints, autonomous error recovery, and conditional branching. Operational Efficiency: Optimize execution paths for latency and token cost without compromising the depth of analysis or system reliability. 3. RAG & Knowledge-Grounded Systems Pipeline Engineering: Architect Retrieval-Augmented Generation (RAG) pipelines that ensure high-fidelity context injection, minimizing hallucinations through rigorous source attribution. Vector Strategy: Manage integration with vector databases (Pinecone, Weaviate, Chroma) and implement advanced retrieval strategies such as semantic re-ranking, query expansion, and context compression. 4. Governance, Evaluation & Optimization Quality Assurance: Define and implement automated evaluation frameworks (LLM-as-a-Judge) to conduct regression testing on prompts and measure output drift. Model Selection: Make strategic decisions regarding model routing (GPT-4 vs. Claude vs. Gemini) and determine the viability of PEFT/LoRA fine-tuning versus context-window optimization. Standardization: Establish strict documentation standards for prompt versioning and reproducibility to ensure enterprise-grade compliance. Candidate Profile Technical Prerequisites: Deep Proficiency: Extensive hands-on experience with LangChain and LangGraph is non-negotiable. LLM Fluency: Mastery of prompt engineering for frontier models (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro). Production Experience: A proven track record of deploying independent AI applications, specifically within HR Tech, Recruitment Automation, or Workflow Orchestration . Architectural Vision: Ability to conceptualize and build end-to-end AI systems, moving beyond isolated prompts to integrated cognitive architectures. Preferred Qualifications: Academic Pedigree: B.Tech/M.Tech from top-tier institutes (IITs, IIITs, BITS, or equivalent global institutions) is highly preferred. Startup DNA: Experience operating in high-velocity, product-first environments where ownership and autonomy are paramount. Desirable Skills (Bonus): Experience with OpenAI Assistants API and Function Calling. Familiarity with LLM observability platforms (LangSmith, Weights & Biases, PromptLayer). Expertise in adversarial prompting and security hardening for LLMs. Application Process Interested candidates are invited to submit their professional profile and a brief portfolio of relevant AI/Agentic projects. Please highlight specific instances where you have engineered complex reasoning flows or automated decision-making systems. Your Experience: Bachelor’s or Master’s degree in Computer Science, AI, or related discipline. Proven experience leading AI projects, particularly in prompt engineering. Strong portfolio or case studies showcasing your work in AI and recruitment automation. Understanding of user-centered design principles and how to apply them in AI settings. Experience collaborating with cross-functional teams to deliver successful AI applications. About Cynet Corp: Cynet Corp is at the forefront of leveraging technology and innovation to enhance workforce solutions. We aim to create powerful AI-driven tools that revolutionize recruitment processes. Together, we can redefine the future of hiring. Visit our website for more information.

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