JOB DESCRIPTION

Lead AI Engineer

JOB RESPONSIBILITIES

Key Responsibilities

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.

JOB QUALIFICATIONS

Minimum Qualifications

10+ years of experience in Data Science, Data Engineering, or Machine Learning.
Bachelor’s Degree in Computer Science, Information Systems, or a related field.
Proficiency in Python (FastAPI, Flask, asyncio), 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 style.

Preferred / Good-to-Have Qualifications

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.

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