AI Engineer – Agentic RAG Systems

What we're looking for

1. RAG Development & Optimization

• Design and implement Retrieval-Augmented Generation pipelines to ground LLMs in enterprise or domain-specific data.
• Make strategic decisions on chunking strategy , embedding models , and retrieval mechanisms to balance context precision, recall, and latency.
• Work with vector databases (Qdrant, Weaviate, pgvector, Pinecone) and embedding frameworks (OpenAI, Hugging Face, Instructor, etc.).
• Diagnose and iterate on challenges like chunk size trade-offs , retrieval quality , context window limits , and grounding accuracy —using structured evaluation and metrics.

2. Chatbot Quality & Evaluation Frameworks

• Establish comprehensive evaluation frameworks for LLM applications, combining quantitative (BLEU, ROUGE, response time) and qualitative methods (human evaluation, LLM-as-a-judge, relevance, coherence, user satisfaction).
• Implement continuous monitoring and automated regression testing using tools like LangSmith , LangFuse , Arize , or custom evaluation harnesses .
• Identify and prevent quality degradation, hallucinations, or factual inconsistencies before production release.
• Collaborate with design and product to define success metrics and user feedback loops for ongoing improvement.

3. Guardrails, Safety & Responsible AI

• Implement multi-layered guardrails across input validation, output filtering, prompt engineering, re-ranking, and abstention (“I don’t know”) strategies.
• Use frameworks such as Guardrails AI , NeMo Guardrails , or Llama Guard to ensure compliance, safety, and brand integrity.
• Build policy-driven safety systems for handling sensitive data, user content, and edge cases with clear escalation paths.
• Balance safety, user experience, and helpfulness , knowing when to block, rephrase, or gracefully decline responses.

4. Multi-Agent Systems & Orchestration

• Design and operate multi-agent workflows using orchestration frameworks such as LangGraph , AutoGen , CrewAI , or Haystack .
• Coordinate routing logic, task delegation, and parallel vs. sequential agent execution to handle complex reasoning or multi-step tasks.
• Build observability and debugging tools for tracking agent interactions, performance, and cost optimization.
• Evaluate trade-offs around latency, reliability, and scalability in production-grade multi-agent environments.

• Strong proficiency in Python (FastAPI, Flask, asyncio) and GCP experience is good to have
• Demonstrated hands-on RAG implementation experience with specific tools, models, and evaluation metrics.
• Practical knowledge of agentic frameworks (LangGraph, LangChain) and evaluation ecosystems (LangFuse, LangSmith).
• Excellent communication skills , proven ability to collaborate cross-functionally , and a low-ego, ownership-driven work >

• Experience in traditional AI/ML workflows — e.g., model training, feature engineering, and deployment of ML models (scikit-learn, TensorFlow, PyTorch).
• Familiarity with retrieval optimization , prompt tuning , and tool-use evaluation .
• Background in observability and performance profiling for large-scale AI systems.
• Understanding of security and privacy principles for AI systems (PII redaction, authentication/authorization, RBAC)
• Exposure to enterprise chatbot systems , LLMOps pipelines , and continuous model evaluation in production.