Agentic AI Professional

6 days ago

Bhavnagar, Gujarat, India beBeeArtificial Full time ₹ 1,00,00,000 - ₹ 2,00,00,000
AI Strategist Role Overview

We are seeking a highly skilled AI strategist to design, build, and deploy production-grade LLM agents and multi-modal AI applications.

About the Position:

This role involves conceptualizing, designing, and implementing LLM-powered agents and NLP solutions tailored to business needs. You will work on building multi-agent and multi-modal AI applications/frameworks, ensuring interactivity, latency optimization, failover, and usability.

Key Responsibilities:

  • Agent & Model 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 LLLMs 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.
  • Issue Resolution & Adaptability:
  • 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.

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.
  • Cloud experience: Azure (preferred), AWS, or GCP.
  • Knowledge of AI orchestration, evaluation, guardrails, and observability tools.

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