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.