Gen AI Lead
Work location: New York City (Hybrid, 3 days in a week)
About the Role
We are seeking an experienced and visionary GenAI Lead to drive the design, development, and implementation of Generative AI solutions across the organization. The successful candidate will lead technical strategy, guide engineering teams, and collaborate with business stakeholders to build secure, scalable, and production-ready AI applications.
The GenAI Lead will work across areas such as large language models, retrieval-augmented generation, AI agents, prompt engineering, model evaluation, and responsible AI. This role requires a strong combination of technical expertise, leadership, business understanding, and the ability to translate emerging AI capabilities into measurable business value.
Key Responsibilities
· Define and execute the organization’s Generative AI technical strategy and roadmap.
· Lead the architecture, design, development, and deployment of GenAI solutions.
· Build production-grade applications using large language models, foundation models, and AI services.
· Design and implement solutions using:
· Retrieval-Augmented Generation (RAG)
· AI agents and agentic workflows
· Prompt engineering and prompt orchestration
· Fine-tuning and model adaptation
· Embeddings and vector databases
· Text generation, summarization, classification, extraction, and conversational AI
· Evaluate and select appropriate commercial, open-source, and cloud-based AI models.
· Develop reusable frameworks, accelerators, APIs, and components for GenAI adoption.
· Lead the integration of GenAI solutions with enterprise applications, data platforms, APIs, and business processes.
· Establish standards for model evaluation, accuracy, reliability, performance, cost, security, and scalability.
· Implement observability, monitoring, feedback loops, and continuous improvement processes for AI applications.
· Ensure responsible AI practices, including privacy, security, fairness, explainability, governance, and regulatory compliance.
· Collaborate with data scientists, software engineers, cloud architects, product managers, cybersecurity teams, and business stakeholders.
· Translate business requirements into practical AI use cases and technical solutions.
· Lead proofs of concept and successfully transition validated solutions into production.
· Mentor and guide engineering teams in GenAI architecture, development, testing, and deployment.
· Stay current with advancements in LLMs, multimodal AI, AI agents, model tooling, and industry best practices.
· Present technical strategies, solution designs, prototypes, and business outcomes to senior leadership.
· Contribute to pre-sales activities, proposals, technical demonstrations, and client engagements when required.
Required Qualifications
· 10+ years of experience in software engineering, data science, machine learning, or AI solution delivery.
· 2+ years of hands-on experience designing and implementing Generative AI solutions.
· Strong experience with Large Language Models and foundation model platforms such as OpenAI, Azure OpenAI, Amazon Bedrock, Google Vertex AI, Anthropic, or open-source models.
· Proven experience building and deploying RAG-based applications and AI assistants.
· Strong programming skills in Python and familiarity with software development best practices.
· Experience with AI/ML frameworks and libraries such as LangChain, LlamaIndex, Hugging Face, PyTorch, TensorFlow, or similar technologies.
· Experience working with vector databases such as Pinecone, FAISS, Weaviate, Milvus, Chroma, or Azure AI Search.
· Strong understanding of APIs, microservices, cloud platforms, databases, and enterprise application integration.
· Experience with model evaluation, prompt optimization, hallucination mitigation, and AI application testing.
· Knowledge of MLOps, LLMOps, CI/CD, containerization, and deployment automation.
· Experience leading technical teams, projects, or enterprise AI initiatives.
· Strong communication, stakeholder management, analytical, and problem-solving skills.
Preferred Qualifications
· Experience developing autonomous or semi-autonomous AI agents and multi-agent systems.
· Experience with multimodal AI, including text, image, audio, or document intelligence.
· Experience with model fine-tuning, parameter-efficient fine-tuning, and synthetic data generation.
· Knowledge of knowledge graphs, graph databases, or semantic search.
· Experience implementing AI governance, security, privacy, and compliance controls.
· Familiarity with cloud services from AWS, Microsoft Azure, or Google Cloud.
· Experience in regulated industries such as financial services, healthcare, insurance, or government.
· Experience with enterprise security concepts, data masking, access controls, and responsible AI frameworks.
· Relevant certifications in cloud, AI, machine learning, or data engineering.
· Experience publishing technical content, presenting at conferences, or contributing to open-source projects.
