EXL

Lead AI Engineer

EXL

full-time

Posted on:

Location Type: Remote

Location: Remote • 🇺🇸 United States

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Job Level

Senior

Tech Stack

FlaskGoogle Cloud PlatformPythonPyTorchScikit-LearnTensorflow

About the role

  • 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.
  • 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.
  • 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.
  • 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.

Requirements

  • 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.
  • 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.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
PythonFastAPIFlaskasyncioData ScienceData EngineeringMachine LearningRAG implementationmodel trainingfeature engineering
Soft skills
communication skillscollaborationownership-driven work stylecross-functional teamwork
Certifications
Bachelor’s Degree in Computer ScienceBachelor’s Degree in Information Systems