Architect

<p><b>"Architect and implement agentic GenAI systems using modern LLM orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen, etc.).</b></p><p><b>Design multi-agent architectures including planner–solver patterns, hierarchical agents, autonomous teams, and goal decomposition workflows.</b></p><p><b>Define end-to-end GenAI architecture covering model selection, orchestration, memory, tools, security, observability, and deployment.</b></p><p><b>Build reusable agent tooling, APIs, and memory architectures for coordination, context persistence, and scalable interaction.</b></p><p><b>Lead hands-on development and production deployment of GenAI applications such as assistants, copilots, workflow automation, and decision-support systems.</b></p><p><b>Evaluate, integrate, and optimize LLMs and multimodal models (OpenAI, Claude, Gemini, Mistral, LLaMA, etc.) and RAG pipelines using vector databases and retrieval systems.</b></p><p><b>Optimize systems for performance, reliability, scalability, cost efficiency, and responsible AI constraints (security, privacy, bias, hallucinations).</b></p><p><b>Establish and enforce GenAIOps / LLMOps practices including evaluation, monitoring, logging, feedback loops, and continuous improvement.</b></p><p><b>Guide and mentor teams on agent design patterns, prompt engineering, tool abstraction, fine-tuning, and best practices.</b></p><p><b>Partner with product, business, and domain stakeholders to translate business requirements into scalable agent-based solutions and AI roadmaps.</b></p><p><b>Drive technology strategy and innovation by tracking advances in multi-agent systems, cognitive architectures, and open-source AI frameworks."</b></p><p><b>"10+ years of overall experience, including 5+ years in Pharma and Biotech, with a strong track record in building domain-specific GenAI agents for Life Sciences use cases.</b></p><p><b>Hands-on expertise in agentic and autonomous AI systems, including multi-agent architectures and frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Haystack, AutoGPT, BabyAGI, CAMEL, and MetaGPT.</b></p><p><b>Strong Python proficiency with deep experience in prompt engineering, tool integration, agent orchestration, RAG pipelines, vector databases, and memory architecture.</b></p><p><b>Practical experience working with commercial and open-source LLMs (GPT-4, Claude, Gemini, Mistral) and implementing fine-tuning, adapters, and RLHF where applicable.</b></p><p><b>Proven ability to design, deploy, and operate cloud-native GenAI solutions on AWS, GCP, and Azure, with focus on latency optimization, autoscaling, and cost control.</b></p><p><b>Demonstrated leadership in architecture design, proof-of-concept development, and hands-on delivery in fast-paced environments.</b></p><p><b>Experience with model cost profiling, real-time performance tuning, and scalability strategies, ensuring reliable and efficient agent behavior.</b></p><p><b>Working knowledge of agent simulation, environment modeling, or reinforcement learning is a plus, along with strong awareness of compliance, privacy, and safety requirements for enterprise GenAI deployments."</b></p>