Job Description:
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.
Must have skillsets:
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, Google Cloud Platform, 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.
Working knowledge of agent simulation, environment modelling, or reinforcement learning is a plus, along with strong awareness of compliance, privacy, and safety requirements for enterprise GenAI deployments.
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