AI Architect

Essential functions

• Model & Agent Design: Conceptualize and design robust NLP solutions and LLM agents tailored to specific business needs, with a focus on user experience, interactivity, latency, failover and functionality.
• Hands-on Coding: Write, test, and maintain clean, efficient, and scalable code for NLP models and AI agents, with a strong emphasis on Python programming.
• Build high quality multi-modal & multi-agents applications/frameworks
• Knowledge on input/output token utilization, prioritization and consumption w.r.t AI agents
• Performance Monitoring: Monitor, optimize LLM agents, implementing model explainability, handling model drift, and ensuring robustness.
• Research Implementation: Ability to read, comprehend, and implement AI Agent  research papers into practical solutions. Stay abreast of the latest academic and industry research to apply cutting-edge methodologies and techniques.
• Debugging & Issue Resolution: Proactively identify, diagnose, and resolve issues related to AI agent, including model inaccuracies, performance bottlenecks, and system integration problems. Utilize debugging tools and techniques to troubleshoot complex problems in model behavior, data inconsistencies, and deployment errors.
• Innovation and Research: Stay updated with the latest advancements in AI agents  technologies, experimenting with new techniques and tools to enhance agent capabilities and performance.
• Continuous Learning: Adaptability to unlearn outdated practices, patterns, technologies and quickly learn and implement new technologies & papers as the ML world evolves. Maintain a proactive approach to staying current with emerging trends and technologies in Agent based solutions (Text & Multi Modal).
• Clear understanding of tool usage and structured outputs in agents
• Clear understanding of speculative decoding and AST-Code RAG
• Clear understanding of Streaming and Sync/Async processing
• Clear understanding of embedding models and their limitations

Qualifications

Programming languages: Python

Public Cloud: Azure

Frameworks: Vector Databases such as Milvus, Qdrant/ ChromaDB, or usage of CosmosDB or MongoDB as Vector stores. Knowledge of AI Orchestration, AI evaluation and Observability Tools. Knowledge of Guardrails strategy for LLM. Knowledge on Arize or any other ML/LLM observability tool.

Experience: Experience in building functional platforms using ML, CV, LLM platforms. Experience in evaluating and monitoring AI platforms in production.

We offer

• Opportunity to work on cutting-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, vision, dental, etc.
• Corporate social events
• Professional development opportunities
• Well-equipped office

About us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.