Generative AI Tech Lead

We are looking for an experienced Generative AI Tech Lead for our client, an international AI development company based in New York, who will collaborate with a team of skilled professionals, including data scientists and software engineers, focusing on the development of advanced backend systems and Generative AI applications.

What You’ll Do

Architect and design scalable AI platform solutions across new and existing enterprise environments, with a focus on Generative AI and agentic workflows.
Lead the development and deployment of AI agents using frameworks such as LangChain and LangGraph.
Design and implement Model Context Protocol-based architectures to enable dynamic tool, system, and data integration for agentic applications.
Own end-to-end architecture for GenAI use cases, including document processing, summarization, and multi-modal workflows across text, images, and tables.
Establish production best practices for accuracy, bias mitigation, hallucination reduction, PII handling, safety guardrails, and model governance.
Evaluate and define key architectural components, including model selection, retrieval strategies, orchestration layers, vector databases, and governance frameworks.
Partner with business and technology teams to identify, design, and scale agentic applications across the organization.
Support deployment and integration within enterprise ML platforms, ensuring solutions are secure, reliable, scalable, and production-ready.
Provide technical leadership, architectural guidance, and hands-on support across AI/ML engineering initiatives.

What You’ll Bring

8 to 10+ years of experience in software engineering, data engineering, AI/ML, or a related technical discipline.
Proven experience architecting enterprise-level AI, ML, or data platforms.
Strong expertise in Generative AI and LLM-based applications, including summarization, retrieval-augmented generation, and multi-modal workflows.
Hands-on experience building and deploying agentic AI workflows, including MCP-based architectures.
Strong Python programming skills, with the ability to complete live coding assessments.
Experience delivering machine learning solutions involving time series analysis, sentiment analysis, topic modeling, or related use cases.
Experience with AWS; AWS certification is preferred.
Strong experience with PySpark, Spark, FastAPI, Kubernetes, and cloud-native deployments.
Experience designing scalable, secure, and governed AI solutions for enterprise environments.

Nice to Haves

Background as a hybrid data scientist and data engineer, with recent focus on LLM-driven solutions.
Experience with vector databases such as Milvus to support embeddings, semantic search, and RAG architectures.
Experience working with cross-functional business teams to translate complex business needs into scalable AI solutions.
Familiarity with enterprise governance, compliance, privacy, and responsible AI practices.

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