GenAI professional

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• Quickly unlearn outdated practices and adapt to emerging GenAI and Agentic AI technologies.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.).