Gen AI Architect
InfoDyne Solutions LLC
New York, NY, United States
3 days ago
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Role details
Contract type
Contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source
Tech stack
LangGraph Framework
Artificial Intelligence
Amazon Web Services
Microsoft Azure
Cloud Computing
Continuous Integration
Decision Support Systems
DevOps
Elasticsearch
Github
Python (Programming Language)
Knowledge Management
+43 more
Knowledge-Based Systems
NoSQL
Performance Tuning
OpenAI
Search Technologies
SQL Databases
Data Logging
Pinecone
Google Cloud
Chatbots
Data Ingestion
LangChain
Retrieval-Augmented Generation
Flask (Web Framework)
Large Language Models
Multi-Agent Systems
Prompt Engineering
Generative AI
Agentic-AI
Fastapi
Gitlab-ci
Kubernetes
Information Technology
CrewAI
Weaviate
AutoGen
Milvus
Machine Learning Operations
FAISS
Claude
Invoking Functions
Google Gemini
Restful APIs
Terraform
Semantic Kernel
Intelligent Document Processing
GPT
Data Pipelines
OpenSearch
Docker
Jenkins
ChromaDB
Microservices
Job description
- Lead the architecture, design, and implementation of enterprise GenAI platforms and applications.
- Design Agentic AI and multi-agent workflows using LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or Llama Index.
- Build and optimize RAG pipelines, vector search solutions, embedding workflows, and enterprise knowledge systems.
- Develop AI solutions for business process automation, knowledge management, intelligent document processing, conversational AI, decision support, and developer productivity.
- Build scalable APIs and microservices using Python, FastAPI, or Flask.
- Implement AI monitoring, observability, security, governance, guardrails, and responsible AI practices.
- Optimize prompts, context management, model performance, latency, reliability, and cost.
- Drive cloud deployment, CI/CD, DevOps, and MLOps practices.
- Collaborate with stakeholders to identify AI opportunities and translate business needs into scalable solutions.
- Mentor engineering teams and establish enterprise AI best practices.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related field, or equivalent practical experience.
- Strong expertise in Generative AI and LLMs, including OpenAI GPT, Claude, Gemini, Llama, or Mistral, with knowledge of prompt engineering, fine-tuning, function calling, tool integration, agent orchestration, context management, and memory handling.
- Proficiency in Python and experience building REST APIs, microservices, and enterprise integrations using FastAPI or Flask.
- Practical experience with RAG architecture, vector databases, embedding models, semantic and hybrid search, and document ingestion pipelines, using technologies such as Pinecone, Weaviate, ChromaDB, FAISS, Milvus, Elasticsearch, or OpenSearch.
- Knowledge of Docker, Kubernetes, CI/CD, GitHub Actions, Jenkins, GitLab CI, Terraform, and cloud platforms such as AWS, Azure, or Google Cloud Platform. Familiarity with MLOps, model deployment, monitoring, logging, AI governance, security, SQL/NoSQL databases, and data pipelines. Strong communication, problem-solving, and stakeholder management skills.
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