Infosys - Principal/Senior Principal Architect - Artificial Intelligence

Description :

About the Role :

We are looking for a highly accomplished Principal / Senior Principal Architect AI to lead the architecture, design, and implementation of enterprise's cale AI platforms, LLM ecosystems, multi agent solutions, and cloud?native AI infrastructure.

The ideal candidate has deep expertise in AI/ML, large language models, distributed systems, and hands-on experience designing robust solutions in Python or Java.

- You will define the long-term AI architecture strategy, guide engineering teams, evaluate emerging technologies, and drive innovation across the AI portfolio.

Key Responsibilities :

AI Architecture & Platform Leadership :

- Own and define the end-to-end AI architecture, including LLM model serving, RAG pipelines, inference scaling, and agentic AI frameworks.

- Architect solutions using LLMs (OpenAI, Azure OpenAI, Anthropic, Meta models, open-source LLMs) for enterprise-grade deployments.

- Lead the design of Agentic AI systems involving autonomous agents, multi-agent coordination, planning loops, and tool-based actions.

- Build scalable architectures in Python or Java, depending on the solution landscape.

Solution Design & Technical Leadership :

- Design distributed, cloud-native, and secure architectures leveraging microservices, APIs, vector databases, and orchestration layers.

- Conduct architecture reviews, evaluate trade-offs, and steer system design to align with business goals.

- Provide technical leadership to AI engineers, data engineers, and solution architects.

- Create reusable components, frameworks, and reference architectures for AI and LLM solutions.

Hands-On Expertise :

- Lead prototyping and POCs using Python or Java for backend services, agent frameworks, or inference pipelines.

- Work with :

- AI/ML frameworks PyTorch, TensorFlow, Scikit-learn

- Agent frameworks LangChain, LangGraph, AutoGen, CrewAI

- Vector DBs Pinecone, Milvus, FAISS, Chroma

- Data engineering tools Spark, Kafka, Databricks

- Drive MLOps, LLMOps, observability, optimization, and model lifecycle management.

Cross-Functional Collaboration :

- Work with product, security, and engineering leaders to ensure architectural alignment.

- Translate business needs into scalable AI platform capabilities.

- Mentor teams on AI best practices, performance engineering, and architectural rigor.

Research & Innovation :

- Evaluate new techniques such as multi-modal models, RLHF, compressed models (LoRA, QLoRA), and advanced retrieval strategies.

- Stay updated on emerging trends in agentic AI, reasoning systems, and scalable LLM runtime architectures.

- Present thought leadership across internal and external forums.

Required Skills & Qualifications :

- 14+ years of experience in software engineering, AI/ML systems, or enterprise architecture.

- Strong coding and architectural expertise in Python or Java (hands-on preferred).

Proven experience building and deploying :

- LLM-powered applications

- RAG pipelines

- Knowledge platforms

- Scalable backend services

- Strong understanding of distributed systems, microservices, caching layers, and cloud-native architectures.

- Experience with Azure / AWS / GCP, Kubernetes, Docker.

- Deep understanding of embeddings, vector stores, inference optimization, and model serving.

- Strong leadership, communication, and stakeholder management skills.

Preferred Skills :

- Experience with LLMOps / MLOps frameworks (Ray, MLflow, KServe, Airflow).

- Hands-on experience with LLM fine-tuning, model compression, and performance optimization.

- Exposure to symbolic reasoning, knowledge graphs, or rule-based decision engines.

- Background in enterprise solutioning for BFSI, Healthcare, Telecom, or large-scale platforms.

- Experience designing agent memory, planning modules, or autonomous task orchestration.

What We Offer :

- Opportunity to lead architecture for cutting-edge AI and Agentic AI platforms.

- High-impact leadership role with visibility across the organization.

- Competitive compensation, benefits, and innovation-driven culture.

- Freedom to explore advanced AI technologies and drive organization-wide strategy.