
Artificial Intelligence Developer
6 days ago
- 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.
- 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).
- 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.
- 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).
- 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.
- Quickly unlearn outdated practices and adapt to emerging GenAI and Agentic AI technologies.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.
- 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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