Architect

Excelra Knowledge Solutions Pvt. Ltd.; Cambridge (MA); On-site; USD 180,000 - 250,000; Full time

Job summary

Excelra Knowledge Solutions Pvt. Ltd. in Cambridge, MA seeks a Consultant to architect and implement agentic GenAI systems using modern LLM orchestration frameworks such as LangChain, LangGraph, CrewAI and AutoGen.

You will design multi-agent architectures, including planner–solver patterns, hierarchical agents, autonomous teams, and goal decomposition workflows. The role covers end-to-end GenAI architecture, model selection, orchestration, memory, tools, security, observability and deployment,

Qualifications

• Architect GenAI systems using multi-agent patterns and orchestration frameworks.
• Hands-on development and deployment of GenAI applications.
• Strong Python and prompt engineering skills with tool integration.
• Experience with cloud platforms and cost optimization for AI workloads.
• 10+ years in AI/GenAI, pharma/biotech domain experience preferred.

Responsibilities

• Architect and implement agentic GenAI systems using modern LLM orchestration frameworks.
• Design multi-agent architectures including planner–solver patterns and autonomous teams.
• 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 and scalable interaction.
• Lead hands-on development and production deployment of GenAI applications such as assistants and workflow automation.
• Evaluate, integrate, and optimize LLMs and multimodal models and RAG pipelines using vector databases.
• Optimize systems for performance, reliability, scalability, cost efficiency and responsible AI.
• Establish GenAIOps / LLMOps practices with monitoring, logging and feedback loops.
• Guide teams on agent design patterns, prompt engineering, tool abstraction and best practices.
• Partner with product and domain stakeholders to translate requirements into AI roadmaps.

Skills

GenAI systems

Multi-agent architecture

LangChain

Python

Prompt engineering

LLM orchestration

RAG pipelines

Vector databases

Memory architecture

Cloud architecture

Performance tuning

Team leadership

Tools

LangChain

LangGraph

CrewAI

AutoGen

Haystack

AutoGPT

BabyAGI

CAMEL

MetaGPT

Job description

Cambridge, Cambridge, Massachusetts, United States

1 - 10 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."

Job Snapshot

Location

Location Cambridge, Cambridge, Massachusetts, United States