GenAI professional
7 hours ago
We are Hiring GenAI professional with proven production-grade project deployment experience with strong expertise in Agentic AI.
About the Role
We are looking for a GenAI professional with strong experience in NLP, Computer Vision, and LLM-based agentic systems. In this role, you will design, build, fine-tune, and deploy production-grade LLM agents and multi-modal AI applications that solve real-world business challenges. You will play a key role in shaping agent design, orchestration, and observability, ensuring enterprise-grade scalability, robustness, and performance.
Key Responsibilities
Model & Agent Design
- 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.
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- Quickly unlearn outdated practices and adapt to emerging GenAI and Agentic AI technologies.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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