AI Prompt Engineering Lead - Agentic AI & Hiring Automation
C
Cynet CorpHR Tech
IndiaContractLead
Salary not disclosed
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Job Details
- Required Skills
- Prompt EngineeringLLMLangChain
Requirements
- 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 work in AI and recruitment automation
- Understanding of user-centered design principles in AI settings
- Experience collaborating with cross-functional teams to deliver successful AI applications
- Extensive hands-on experience with LangChain
- Extensive hands-on experience with LangGraph
- Mastery of prompt engineering for frontier models (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro)
- Proven track record of deploying independent AI applications within HR Tech, Recruitment Automation, or Workflow Orchestration
- Ability to conceptualize and build end-to-end AI systems
- B.Tech/M.Tech from top-tier institutes (IITs, IIITs, BITS, or equivalent global institutions) highly preferred
- Experience operating in high-velocity, product-first environments
Responsibilities
- Engineer production-grade prompt infrastructures for complex workflows including candidate evaluation, resume parsing, interview automation, and autonomous stakeholder communication.
- Deploy advanced prompting paradigms (Chain-of-Thought, Tree-of-Thought, Self-Consistency, Instruction Hierarchies) to ensure high-precision reasoning.
- Architect robust guardrails and instruction-following protocols to maintain system safety, prevent jailbreaks, and ensure strict adherence to hiring rubrics.
- Build and manage stateful, multi-agent workflows using LangGraph and LangChain.
- Design complex, multi-step decision trees that incorporate human-in-the-loop (HITL) checkpoints, autonomous error recovery, and conditional branching.
- Optimize execution paths for latency and token cost without compromising depth of analysis or system reliability.
- Architect Retrieval-Augmented Generation (RAG) pipelines for high-fidelity context injection and minimal hallucinations.
- Manage integration with vector databases (Pinecone, Weaviate, Chroma) and implement advanced retrieval strategies.
- Define and implement automated evaluation frameworks (LLM-as-a-Judge) for regression testing and measuring output drift.
- Make strategic decisions regarding model routing (GPT-4 vs. Claude vs. Gemini) and PEFT/LoRA fine-tuning versus context-window optimization.
- Establish strict documentation standards for prompt versioning and reproducibility.
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