AI Systems Specialist

6 days ago

Bengaluru, Karnataka, India beBeeAgentic Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Role Overview

We are seeking a skilled AI engineer with expertise in Large Language Model (LLM) technology to design, build, and fine-tune sophisticated agentic systems.

About the Position

This role requires strong knowledge of Natural Language Processing (NLP), Computer Vision, and LLM-based agentic systems. You will be responsible for shaping agent design, orchestration, and observability, ensuring enterprise-grade scalability, robustness, and performance.

Main Responsibilities:
  • Agentic System Development
    • Conceptualize, design, and implement LLM-powered agents and NLP solutions tailored to business needs.
    • Build 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.
  • Coding & Integration
    • 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.
  • Troubleshooting & Improvement
    • 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.
  • Lifelong Learning
    • Quickly unlearn outdated practices and adapt to emerging GenAI and Agentic AI technologies.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.

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