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Develop and deploy complex agentic and multimodal AI workflows at production scale, architecting memory-enabled, context-aware agents that drive dynamic automation, personalization, real-time monitoring and content generation.
Design and orchestrate advanced LLM and RAG-driven agent solutions using modular frameworks including LangGraph, Langfuse, CrewAI, AutoGen, and other emerging agentic orchestration tools—enabling adaptive, stateful, and highly interactive workflow graphs.
Integrate and optimize complete multimodal GenAI pipelines: text-to-image, image-to-video, text-to-video, and text-to-voice, leveraging and extending in-house and third party models.
Fine-tune (parameter-efficient tuning, LoRA, QLoRA, etc.), and evaluate (perplexity, accuracy, creativity metrics) both open-source (Llama, MPT, Phi-4, etc.) and commercial LLMs (OpenAI GPT-4, Gemini, Claude).
Pioneer LLM-based simulation & evaluation environments (e.g., LLM-as-a-Judge): Leverage the latest LLMs, multi-agent systems, and generative tools to simulate user behavior, accelerate experimental cycles, and rigorously evaluate new features or workflows in both synthetic and prod online shopping and travel environments.
Simulate A/B testing via LLM simulations: Build A/B test agents capable of simulating user engagement, behavioral outcomes, and marketing scenarios to complement and replace large-scale online testing—enabling rapid iteration, robust pre-deployment validation, and deeper insight into agentic/LLM-driven feature impact.
Continuously adopt and iterate on the latest frameworks, staying current with state-of-the-art in agentic orchestration, memory models, multimodal GenAI, LLM pipelines, and LLM operation (RAG, memory, tracing, evaluation).
Collaborate deeply with engineering, marketing, and product teams to translate evolving business objectives into scalable and observable AI-driven solutions.
Requirements
Minimum 7+ years experience delivering robust, scalable AI/GenAI solutions at enterprise scale with a Bachelors degree (4-5+ year of relevant experience with a Masters/PhD in CS, EE, Stats)
Agentic frameworks experience working with platforms such as LangGraph, Langfuse, RAG, CrewAI, AutoGen, and comparable agent workflow frameworks
Ability to design complex agent graphs with memory, tool calling, and multi-step reasoning
Multimodal and agentic AI experience in both commercial and open-source models (VLMs, CLIP, Phi-4, DINO, etc), advanced LLM pipeline design, and shipping both inference and generative AI features to users
Hands-on experience in fundamental neural network models (CNN, Transformers, BERT, VAEs, Multimodals etc)
Proven expertise in architecting persistent, hybrid, and context-managed memory for agentic workflows
Strong in Python (PyTorch, HuggingFace, etc.), MLOps, distributed systems (PySpark, Databricks, Airflow), and scalable API engineering
Deep experience with tracing, logging, and observability stacks (Langfuse, PromptLayer, Weights & Biases, etc.), including prompt monitoring and model evaluation in live production
Ability to translate complex technical systems into actionable solutions for cross-functional teams, leveraging Multimodal LLMs and Gen AI models
