Senior LLM Systems Engineer

5 days ago

Mount Abu, Rajasthan, India beBeeApplication Full time ₹ 25,00,000 - ₹ 30,00,000

AI Application Design Engineer

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We are seeking a skilled AI application design engineer to drive the development and deployment of cutting-edge AI applications.

About the Role

This role is instrumental in shaping our AI strategy, focusing on NLP, Computer Vision, and LLM-based agentic systems. The ideal candidate will have extensive experience in production-grade project deployments, strong expertise in Agentic AI, and proficiency in Python.

Key Responsibilities

Model & Agent Design

  • Design, implement, and fine-tune LLM-powered agents and NLP solutions tailored to business needs.
  • Develop multi-agent and multi-modal AI applications/frameworks, ensuring interactivity, latency optimization, failover, and usability.
  • Apply advanced design principles for structured outputs, tool usage, speculative decoding, AST-Code RAG, streaming, and async/sync processing.

Hands-on Coding & Development

  • Write, test, and maintain clean, scalable, and efficient Python code for LLMs and AI agents.
  • Implement fine-tuning, embeddings, and prompt engineering with a focus on cost, latency, and accuracy.
  • Integrate models with vector databases (Milvus, Qdrant, ChromaDB, CosmosDB, MongoDB).

Performance & Monitoring

  • Monitor and optimize LLM agents for latency, scalability, robustness, and explainability.
  • Implement observability and guardrails strategies for enterprise-safe AI deployments.
  • Handle model drift, token consumption optimization, and error recovery mechanisms.

Research & Innovation

  • Read, interpret, and implement AI/Agent research papers into practical production-ready solutions.
  • Stay ahead of academic and industry trends in Agentic AI, multimodal AI, orchestration frameworks, and evaluation methodologies.
  • Experiment with new AI orchestration tools, evaluation frameworks, and observability platforms (Arize or similar).

Debugging & Issue Resolution

  • Diagnose and resolve model inaccuracies, system integration issues, and performance bottlenecks.
  • Apply advanced debugging techniques to troubleshoot deployment errors, data inconsistencies, and unexpected agent behaviors.

Continuous Learning & Adaptability

  • Quickly unlearn outdated practices and adapt to emerging GenAI and Agentic AI technologies.
  • Contribute to a culture of innovation by experimenting, prototyping, and scaling cutting-edge AI solutions.

Required Skills & Experience

  • 5–9 years total experience, with 4+ years in NLP, CV, and LLMs.
  • Strong expertise in GenAI, LLMs, RAG pipelines, embeddings, and vector databases.
  • Proficiency in Python with strong debugging and system design skills.
  • Hands-on experience with Agentic AI frameworks (LangChain Agents, AutoGen, CrewAI, Temporal, DSPy).
  • Proven record of production-grade AI deployments (not just POCs).
  • Cloud experience: Azure (preferred), AWS, or GCP.
  • Knowledge of AI orchestration, evaluation, guardrails, and observability tools (Arize, Weights & Biases, etc.).

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