Principal Engineer -- AI Architect

Nagarro•New York City, NY

About The Position

We are looking for a seasoned AI Architect / Principal Data Scientist to join our AI & Tech Accelerator. In this role, you will lead the design, architecture, and enterprise-scale deployment of advanced AI systems, leveraging Machine Learning, Generative AI, and emerging Agentic AI paradigms. You will play a critical role in shaping AI strategy, driving innovation at scale, and enabling business transformation through cutting-edge intelligent systems. Key Responsibilities : In this role, you will operate at the intersection of strategy, architecture, and execution, driving enterprise-wide AI adoption.

Requirements

Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related field
11–13 years of experience in AI/ML, with significant experience in designing and deploying large-scale AI systems
Proven experience in architecting enterprise-grade AI/ML platforms and solutions
Strong expertise in Machine Learning, Deep Learning, and statistical modeling
Experience designing scalable ML systems and production pipelines
Deep expertise in LLMs (GPT, Claude, Llama, Gemini) and their enterprise applications
Strong experience in RAG architectures, prompt engineering, context engineering, embeddings, and vector databases
Hands-on experience with agentic frameworks (LangGraph, CrewAI, AutoGen) and multi-agent orchestration
Strong system design and architecture skills for distributed AI systems
Expertise in Python, SQL, and software engineering best practices
Experience with microservices, APIs, and scalable backend systems
Strong experience in MLOps practices including CI/CD, model versioning, monitoring, and governance
Hands-on experience with tools such as MLflow, Vertex AI, Kubeflow, or similar
Deep experience with cloud platforms, especially GCP (Vertex AI, BigQuery) and/or Databricks
Strong understanding of data lakes, data warehouses, and real-time data processing
Strategic Leadership : Ability to align AI initiatives with business goals and drive long-term technology vision
Ownership & Accountability: Takes end-to-end ownership of architecture, delivery, and outcomes
Influence & Communication: Strong ability to communicate complex technical concepts to senior stakeholders and executives
Innovation Mindset: Continuously explores emerging AI trends and drives adoption of next-gen technologies
Data Modeling (Strong)
Generative & Agentic AI
Machine Learning
Python (Expert)
Embeddings & Vector Databases

Responsibilities

Architect & Scale AI Solutions: Design end-to-end AI/ML architectures, including scalable, secure, and production-ready systems using Machine Learning, Deep Learning, and Large Language Models (LLMs). Establish best practices for building robust and reusable AI platforms.
Lead Generative & Agentic AI Innovation: Define and implement enterprise-grade Generative AI and Agentic AI solutions, including multi-agent systems, autonomous workflows, and LLM orchestration frameworks. Drive innovation in areas such as RAG, context engineering, and intelligent automation.
Enterprise Impact & Transformation: Own and deliver scalable AI solutions across multiple business domains (marketing, supply chain, retail, operations), ensuring measurable business impact and alignment with organizational strategy.
Technical Leadership & Governance : Act as a technical authority defining architecture standards, AI governance, model lifecycle management, and responsible AI practices. Ensure scalability, security, and compliance across all AI initiatives.
Cross-functional Leadership: Collaborate with senior stakeholders, product leaders, data engineers, and platform teams to translate business problems into AI-driven solutions. Lead architecture discussions and influence key technical decisions.
Mentorship & Capability Building : Mentor senior engineers and data scientists, drive knowledge sharing, and build a strong AI engineering culture. Lead communities of practice and contribute to organizational capability development.
Define AI roadmap and contribute to organizational AI strategy
Establish reusable frameworks, accelerators, and best practices
Drive cost optimization and performance efficiency at scale
Ensure ethical AI and responsible AI implementation