Hi
Hope you are doing well !!
I have an urgent position. Kindly go through the Job description and let me know if this would be of interest to you.
Role : Senior AI Architect (Hybrid)
Location: Roseland, NJ (Must be local to NJ/NY)
Duration : 12 Months
Note : Must have LinkedIn
Job Description:
- Our Client is embarking on the next phase of its AI journey, focused on building scalable, impactful AI solutions across its product portfolio. We are seeking a highly motivated and execution-focused AI Architect to play a pivotal role in this transformation. This individual will be responsible for translating strategic AI architectural recommendations centered around a unified Knowledge + Data + Backend (KDB) layer into tangible, implementable solutions.
- The ideal candidate possesses deep technical expertise in designing and building scalable AI systems, particularly agentic ecosystems, and thrives in an environment that values rapid experimentation, iteration, and delivery. You will be instrumental in ironing out the details, driving the technical execution, and ensuring the successful realization of Our Client's target AI architecture.
Responsibilities
- Architectural Design & Implementation: Design, document, and oversee the implementation of Our Client's target AI architecture, focusing on the KDB layer, product-agnostic semantic layer, reusable application (RApp) layer, and agentic components.
- Execution & Delivery: Translate high-level designs into detailed technical specifications and deliverables. Drive timely and high-quality delivery of foundational AI components and services.
- Agent Ecosystem Development: Lead the design and development of a scalable, reusable, and product-agnostic agentic ecosystem based on the KDB layer.
- Knowledge & Data Integration: Architect solutions to integrate diverse knowledge sources (e.g., source code, API specs, structured data) into a semantically rich layer. Design automation for knowledge extraction and lifecycle management.
- Experimentation & Prototyping: Rapidly prototype, evaluate technologies (e.g., LLMs, vector DBs, agent frameworks), and iterate based on feedback. Define metrics to evaluate architectural choices and performance.
- Technical Leadership & Collaboration: Provide guidance to engineering and data science teams. Collaborate with product managers, software engineers, and other stakeholders to align on architecture and delivery.
- Strategy to Execution Bridge: Serve as the key technical liaison, ensuring implementation aligns with strategic vision. Communicate architectural concepts clearly to diverse audiences.
- Risk Management & Scalability: Identify and address technical risks related to scalability, performance, security, and maintainability.
Qualifications
- Experience: Proven success in designing and implementing complex, large-scale software and AI/ML systems.
- AI/ML Expertise: Strong understanding of ML frameworks (e.g., TensorFlow, PyTorch), NLP, LLMs, and AI/ML principles.
- Architecture Skills: Expertise in microservices, APIs, distributed systems, cloud-native patterns, and software architecture for scalability and performance.
- Agent Systems: Experience with agent-based or multi-agent ecosystems.
- Data Engineering: Proficiency with data pipelines, SQL/NoSQL databases, and data warehousing.
- Programming: Skilled in languages such as Python, Java, or Scala.
- Execution Focus: Strong ability to convert strategy into technical implementation and deliver results.
Preferred Qualifications
- Experience building production-grade AI solutions.
- Knowledge of semantic technologies, knowledge graphs, and ontologies.
- Familiarity with source code analysis or API-driven knowledge extraction.
- Understanding of MLOps tools and practices.
- Experience working in agile software development environments.
- Experience leading or mentoring technical teams.
If you are interested, please share your updated resume and suggest the best number & time to connect with you