Gen AI Lead

Job Description

Role: Gen AI Lead

\n

Location: New York City, NY – 3days/week onsite AND In person interview is must

\n

\n

We are seeking a hands-on GenAI Lead / Architect to design, develop, and deploy enterprise-scale Generative AI solutions. The role focuses on Agentic AI, RAG architectures, LLM orchestration, workflow automation, and scalable AI platform engineering.

\n

\n

The Role

\n

Responsibilities:

\n

\n
• Lead the architecture, design, and implementation of enterprise GenAI platforms and applications.
\n
• Design Agentic AI and multi-agent workflows using LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or Llama Index.
\n
• Build and optimize RAG pipelines, vector search solutions, embedding workflows, and enterprise knowledge systems.
\n
• Develop AI solutions for business process automation, knowledge management, intelligent document processing, conversational AI, decision support, and developer productivity.
\n
• Build scalable APIs and microservices using Python, FastAPI, or Flask.
\n
• Implement AI monitoring, observability, security, governance, guardrails, and responsible AI practices.
\n
• Optimize prompts, context management, model performance, latency, reliability, and cost.
\n
• Drive cloud deployment, CI/CD, DevOps, and MLOps practices.
\n
• Collaborate with stakeholders to identify AI opportunities and translate business needs into scalable solutions.
\n
• Mentor engineering teams and establish enterprise AI best practices.
\n

\n

\n

\n

Requirements:

\n

\n
• Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related field, or equivalent practical experience.
\n
• 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.
\n
• Proficiency in Python and experience building REST APIs, microservices, and enterprise integrations using FastAPI or Flask.
\n
• 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.
\n
• Knowledge of Docker, Kubernetes, CI/CD, GitHub Actions, Jenkins, GitLab CI, Terraform, and cloud platforms such as AWS, Azure, or GCP. Familiarity with MLOps, model deployment, monitoring, logging, AI governance, security, SQL/NoSQL databases, and data pipelines. Strong communication, problem-solving, and stakeholder management skills.
\n

\n