Architect

Excelra Knowledge Solutions Pvt. Ltd.; Cambridge (MA); On-site; USD 170,000 - 250,000; Full time

Job summary

Excelra Knowledge Solutions Pvt. Ltd. seeks an experienced GenAI Architect in Cambridge to design agentic systems and multi-agent architectures using frameworks like LangChain and LangGraph.

You will drive end-to-end GenAI strategy, model orchestration, memory, tools, and security for scalable enterprise solutions. Lead hands-on development, deploy GenAI apps such as copilots and workflow automations, and mentor teams on prompts, tooling, and best practices.

Qualifications

• 10+ years of overall experience in GenAI or AI architecture.
• 5+ years building domain-specific GenAI agents for Life Sciences or similar industries.
• Hands-on experience with multi-agent systems and orchestration frameworks.
• Experience with cloud-native deployments and cost management.

Responsibilities

• Architect and implement agentic GenAI systems and multi-agent architectures.
• Define end-to-end GenAI architecture covering models, tools, memory, and security.
• Lead hands-on development and deployment of GenAI applications.
• Mentor teams on design patterns, prompts, and tool integration.
• Guide product and domain stakeholders to scalable AI roadmaps.

Skills

GenAI Architect

LangChain

LangGraph

CrewAI

Python

Prompt engineering

RAG pipelines

LLMOps

Education

Bachelor’s or higher in CS/Engineering

Tools

AWS

GCP

Azure

Vector databases

Job description

Cambridge, Cambridge, Massachusetts, United States

8 - 14 Years

Consultant

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."


• "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.


• 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."

Location Cambridge, Cambridge, Massachusetts, United States