Job Description

Location: New York City, NY (100% Onsite)

Position Type: Long-term Contract

Interview Process: In-Person Interview Required

About The Role

We are seeking a Lead Generative AI Engineer to drive the architecture, design, and deployment of enterprise-grade GenAI platforms and Agentic AI solutions. In this role, you will lead hands-on implementation, establish enterprise AI governance and MLOps practices, and build scalable multi-agent systems and advanced RAG pipelines.

Key Responsibilities

Architecture & Strategy: Lead the architecture, design, and implementation of scalable enterprise GenAI platforms, multi-agent systems, and business process automation solutions.
Agentic Workflows: Design and implement agentic AI and multi-agent workflows utilizing frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or LlamaIndex.
RAG & Search Solutions: Build and optimize end-to-end Retrieval-Augmented Generation (RAG) pipelines, vector search strategies, hybrid search implementations, and document ingestion systems.
Backend & API Engineering: Develop scalable RESTful APIs and microservices using Python, FastAPI, or Flask.
Optimization & Performance: Optimize prompt engineering, context management, model latency, cost, and reliability across frontier and open-source models.
Observability & Governance: Implement enterprise AI guardrails, responsible AI frameworks, safety controls, monitoring, and observability.
DevOps & MLOps: Drive cloud deployment, CI/CD automation, model serving, and infrastructure management across AWS, Azure, or GCP.
Leadership & Collaboration: Partner with cross-functional business stakeholders to translate requirements into AI solutions while mentoring engineering teams on AI best practices.

Required Skills & Experience

Education: Bachelor s or Master s degree in Computer Science, Artificial Intelligence, Engineering, or equivalent practical experience.
Generative AI & LLMs: Deep hands-on expertise with major LLMs (OpenAI GPT, Claude, Gemini, Llama, Mistral) including fine-tuning, function calling, tool usage, memory management, and agent orchestration.
Frameworks & Search: Proven experience with agentic frameworks and vector databases (Pinecone, Weaviate, ChromaDB, FAISS, Milvus, Elasticsearch, or OpenSearch).
Python Development: Strong expertise in Python and microservice architectures (FastAPI, Flask) along with SQL/NoSQL data store integrations.
Cloud & DevOps: Hands-on experience with Docker, Kubernetes, Terraform, CI/CD pipelines (GitHub Actions, Jenkins, or GitLab CI), and major cloud platforms (AWS/Azure/GCP).
Soft Skills: Exceptional problem-solving abilities, communication skills, and leadership capabilities for client and stakeholder engagements.

Preferred Qualifications

Experience building AI Copilots, conversational interfaces, and intelligent document processing workflows.
Familiarity with the Model Context Protocol (MCP).
Knowledge of workflow orchestration systems such as Apache Airflow, n8n, Temporal, or Camunda.
Hands-on experience with frontend technologies like Angular, TypeScript, or React.
Deep understanding of AI security, model evaluation frameworks, and responsible AI governance.

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