GenAI Architect - Enterprise AI Leader

Synechron; New York (NY); On-site; USD 115,000 - 130,000; Full time

Benefits offered by this job

Global offices abroad

Paid time off

Insurance plan

Retirement plan

Udemy for Business

Training opportunities

Diversity & inclusion initiatives

Job summary

Synechron is seeking a hands-on GenAI Lead / Architect in New York to design, develop, and deploy enterprise-scale Generative AI solutions. You will architect Agentic AI, RAG pipelines, and LLM orchestration, while driving DevOps and MLOps practices.

The role requires API/microservice development in Python, strong AI governance, and collaboration with stakeholders to translate business needs into scalable AI platforms.

Qualifications

• Bachelor’s or Master’s degree in CS/Engineering/AI or related field.
• Strong Generative AI and LLM knowledge with prompt engineering and tool integration.
• Proficiency in Python and building REST APIs/microservices.

Responsibilities

• Lead architecture and delivery of enterprise GenAI platforms.
• Design Agentic AI and multi-agent workflows.
• Build and optimize RAG pipelines and knowledge systems.

Skills

Generative AI

LLMs

Python

FastAPI/Flask

RAG architectures

LangChain

AI governance

Security

CI/CD

DevOps

MLOps

Cloud platforms

AWS

Azure

GCP

GitHub Actions

Jenkins

Docker

Kubernetes

Vector databases

Pinecone

Weaviate

ChromaDB

FAISS

Milvus

Elasticsearch

OpenSearch

SQL/NoSQL

Data pipelines

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or related field

Tools

Docker

Kubernetes

Terraform

LangChain

LangGraph

CrewAI

AutoGen

OpenAI GPT

Claude

Gemini

Llama Index

Weaviate

Pinecone

FAISS

Milvus

OpenSearch

Elasticsearch

FastAPI

Flask

Job description

Synechron is seeking a hands-on GenAI Lead / Architect in New York to design, develop, and deploy enterprise-scale Generative AI solutions. You will architect Agentic AI, RAG pipelines, and LLM orchestration, while driving DevOps and MLOps practices.

The role requires API/microservice development in Python, strong AI governance, and collaboration with stakeholders to translate business needs into scalable AI platforms.