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Architect

Excelra

This job is closed: the employer no longer lists it.

Skills

Required

  • GenAI
  • agentic and autonomous AI systems
  • multi-agent architectures
  • frameworks
  • LangChain
  • LangGraph
  • CrewAI
  • AutoGen
  • Haystack
  • CAMEL
  • Python
  • Azure

Preferred

  • agent simulation / environment modeling / reinforcement learning
  • privacy
  • GenAI deployments
  • modern LLM orchestration frameworks
  • Design multi-agent architectures
  • GenAI architecture

Responsibilities

  • Architect and implement agentic GenAI systems using modern LLM orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen, etc.).
  • Design multi-agent architectures including planner–solver patterns, hierarchical agents, autonomous teams, and goal decomposition workflows.
  • Define end-to-end GenAI architecture covering model selection, orchestration, memory, tools, security, observability, and deployment.
  • Build reusable agent tooling, APIs, and memory architectures for coordination, context persistence, and scalable interaction.
  • Lead hands-on development and production deployment of GenAI applications such as assistants, copilots, workflow automation, and decision-support systems.
  • Evaluate, integrate, and optimize LLMs and multimodal models (OpenAI, Claude, Gemini, Mistral, LLaMA, etc.) and RAG pipelines using vector databases and retrieval systems.
  • Optimize systems for performance, reliability, scalability, cost efficiency, and responsible AI constraints (security, privacy, bias, hallucinations).
  • Establish and enforce GenAIOps / LLMOps practices including evaluation, monitoring, logging, feedback loops, and continuous improvement.
  • Guide and mentor teams on agent design patterns, prompt engineering, tool abstraction, fine-tuning, and best practices.
  • Partner with product, business, and domain stakeholders to translate business requirements into scalable agent-based solutions and AI roadmaps.
  • Drive technology strategy and innovation by tracking advances in multi-agent systems, cognitive architectures, and open-source AI frameworks.

Requirements

  • 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.
  • 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.
  • Strong Python proficiency with deep experience in prompt engineering, tool integration, agent orchestration, RAG pipelines, vector databases, and memory architecture.
  • Practical experience working with commercial and open-source LLMs (GPT-4, Claude, Gemini, Mistral) and implementing fine-tuning, adapters, and RLHF where applicable.
  • 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.
  • Demonstrated leadership in architecture design, proof-of-concept development, and hands-on delivery in fast-paced environments.
  • Experience with model cost profiling, real-time performance tuning, and scalability strategies, ensuring reliable and efficient agent behavior.

Preferred

  • 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.