# AI Prompt Engineering Lead (Agentic AI & Hiring Automation) - Remote

> Cynet Corp · India (Remote) · — · Posted 2026-01-06

**Workplace:** remote

**Department:** Cynet India

## Description

**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.

## Requirements

**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.

## Apply

[Apply at Cynet Corp](https://apply.workable.com/cynet-corp/j/9E7D373310/apply)

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