Role & Responsibilities

• Build Agentic Workflows: Design and deploy AI agents capable of reasoning and decision-making using frameworks like LangChain or LlamaIndex.
• Architect Voice AI Systems: Work on low-latency, real-time conversational bots for candidate outreach using WebSockets, STT, TTS, ensuring natural state management and context retention.
• Engineer Robust Data Pipelines: Build parsing modules that enforce LLMs to return strict JSON schemas for resume data extraction and implement cleaning pipelines for unstructured data (PDF/DOCX).
• Implement Advanced RAG: Develop retrieval systems using Pgvector or Pinecone that utilize Hybrid Search (semantic + keyword) to ensure accurate job-to-candidate matching.
• Productionize & Observe: Set up tracing and observability using tools like LangSmith to debug complex chains, monitor token usage, and optimize costs.
• Backend Integration: Wrap AI logic into scalable, asynchronous microservices using Python (FastAPI) and containerize them with Docker.

Technical Requirements

1. Generative AI & LLM Engineering

• Structured Outputs: Proven experience forcing LLMs to output valid JSON schemas via function calling (essential for data parsing tasks).
• Prompt Engineering: Deep understanding of prompting strategies (Chain-of-Thought, Few-Shot) and ability to design robust system prompts that handle edge cases gracefully.
• Orchestration: Hands-on experience building complex chains and retrieval loops using LangChain or LlamaIndex.

2. Voice AI & Real-Time Processing

• Audio Stack: Experience with STT/TTS APIs (Whisper, Deepgram).
• Streaming & Latency: Mastery of WebSockets and asynchronous programming to handle streaming audio with sub-second latency.
• State Management: Ability to architect conversation managers that maintain "memory" history, and prior answers during a live call.

3. Search & Data (RAG)

• Vector Databases: Proficiency with vector stores like Pgvector (Supabase), Pinecone, or Qdrant.
• Ingestion: Experience constructing pipelines for chunking and cleaning unstructured documents.

4. MLOps & Production Engineering

• Observability: Experience tracking traces, latency, and errors using LangSmith.
• Evals: Ability to write automated evaluation scripts ("unit tests for AI") to verify prompt performance against datasets before deployment.
• Cost Optimization: Experience monitoring token consumption and implementing strategies to balance intelligence vs. cost (e.g., routing simpler tasks to smaller models).

5. Core Backend

• Python: Skills, specifically with FastAPI.
• Async/Concurrency: Mastery of async/await patterns to handle concurrent resume parsing and multiple voice calls simultaneously.
• Infrastructure: Proficiency with Docker and basic SQL for relational data management.

6. Machine Learning & Algorithms (Good to have)

• Recommendation Logic: Understanding of core matching concepts beyond just embeddings (e.g., Collaborative Filtering, Matrix Factorization, or Two-Tower Architecture).
• Ranking & Scoring: Experience implementing Learning to Rank (LTR) or Re-ranking strategies (Cross-Encoders) to sort thousands of candidates accurately.
• Predictive Modeling: Familiarity with traditional ML libraries (Scikit-learn, XGBoost) to build classification models (e.g., "Predicting candidate joining probability").

The "Applied Mindset" We Need:

• Model Strategist: You know when to use GPT-4o and when to use a cheaper, faster model like GPT-4o-mini or a local Llama instance, to balance cost, latency, and intelligence effectively.
• Security First: You understand the risks of Prompt Injection and Jailbreaking, especially in public-facing interview bots, and know how to mitigate them.
• Hallucination Mitigation: You don't trust the model blindly. You use grounding techniques to ensure the AI sticks to the provided facts.