Role: Senior Generative AI Lead/ Architect Locations: Whippany, NJ (Hybrid Onsite) Duration: 12+ Months Contract
F2F Interview Highly Preferred For Local Candidates
Note: Candidate needs to be in the office 3-4 Days every week. Local or candidates from adjacent states only.
Job Description:
We are seeking a Senior Generative AI Lead to define build and scale enterprisegrade Generative AI solutions
This role sits at the intersection of AI strategy handson engineering and business transformation
You will lead GenAI initiatives endtoendfrom ideation and architecture to production deployment while mentoring teams and shaping the organizations AI roadmap
This is a senior highimpact role suited for a leader who can translate business problems into scalable GenAI solutions and guide stakeholders through responsible secure and valuedriven AI adoption
Key Responsibilities:
Strategy Leadership:
Define and own the Generative AI strategy and roadmap aligned to business priorities
Identify highvalue use cases across domains such as operations risk compliance customer experience and engineering productivity
Act as a thought leader on GenAI advising senior stakeholders on capabilities limitations and ROI
Establish AI governance responsible AI practices and model risk controls for enterprise use
Architecture Solution Design
Design and oversee endtoend GenAI architectures including
Large Language Models LLMs
RetrievalAugmented Generation RAG
Agentic and multiagent workflows
Lead decisions on model selection finetuning prompt engineering and inference optimization
Ensure solutions meet enterprise standards for scalability security performance and observability
Hands-on Development
Lead and review implementation of GenAI solutions using Python and modern ML frameworks
Build and deploy APIs and services integrating GenAI into existing platforms and workflows
Partner with cloud teams to deploy solutions on AWS, Azure or Google Cloud Platform using managed GenAI services
Guide teams through productionization including monitoring evaluation and continuous improvement
Collaboration Enablement
Collaborate closely with Product Data Science Engineering Security Legal and Compliance teams
Mentor and upskill engineers data scientists and analysts in GenAI best practices
Establish reusable frameworks reference architectures and accelerators for GenAI adoption
Support client or internal stakeholder engagements including demos pilots and scaleout programs"
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