Principal Engineer

Company: People Impact

LinkedIn: Visit LinkedIn

Business Type: Startup

Company Type: Service

Business Model: B2B

Funding Stage: Pre-seed

Industry: Fintech

Salary Range: ₹ 35-45 Lacs PA

Job Description

We are seeking an experienced Java Solution Architect to design, define, and govern scalable, secure, and cloud-native enterprise architectures.

The ideal candidate will have strong expertise in Java and Spring technologies, proven architectural leadership, and hands-on experience with AI-Native architecture patterns, including RAG, LLMs, Agentic AI frameworks, and cloud-based AI services.

The role will involve working closely with engineering, QE, DevOps, and client teams to drive technical strategy, architecture standards, and successful delivery of enterprise solutions.

Key Responsibilities

• Design and own scalable, resilient, and cloud-native application architectures for enterprise Java platforms.
• Define and document High-Level Design (HLD) and Low-Level Design (LLD) for complex systems and applications.
• Lead architecture reviews, technical design discussions, and code reviews across development squads.
• Establish and enforce coding standards, architectural best practices, design patterns, and engineering principles.
• Provide technical leadership, mentorship, and guidance to development and QE teams.
• Collaborate with onshore client and partner teams to understand requirements, align priorities, and drive project milestones.
• Design and implement AI-Native architectures leveraging:
• Retrieval-Augmented Generation (RAG)
• Embeddings and vector-based retrieval
• LLM integrations and deployments
• Agentic AI frameworks
• AI application memory management strategies
• Leverage Azure AI and Azure OpenAI services for enterprise GenAI solutions and deployment.
• Define strategies for memory management, context handling, and knowledge retrieval in AI-powered applications.
• Drive DevOps automation, CI/CD, and deployment practices across application teams.
• Work closely with security, infrastructure, and platform teams to ensure solutions meet enterprise security, scalability, reliability, and compliance requirements.
• Provide technical direction across multiple squads and contribute to organization-wide architecture initiatives.
  

Required Skills & Experience

• 9+ years of overall software engineering experience, with 13+ years preferred.
• 3–5+ years of experience in a Solution/Technical Architect role, designing both on-premises and cloud-based systems.
• Expert-level knowledge of:
• Java
• Spring Boot
• REST APIs
• Spring Batch
• Strong experience in microservices architecture, distributed systems, and enterprise application design.
• 3+ years of hands-on experience with AI-Native architecture, including:
• RAG
• LLMs
• Embeddings
• Agentic frameworks
• GenAI integrations
• AI application memory management
• Cloud AI services
• Hands-on experience with Azure OpenAI, GenAI solution development, and LLM/model fine-tuning or customization.
• Strong understanding of HLD/LLD, architecture patterns, design principles, and technical documentation.
• Experience with DevOps and engineering tools, including Maven, Git, CI/CD, and Autosys.
• Good understanding of cloud architecture, security, scalability, and deployment models.
• Good to have experience with RPM packaging and Linux environments.
• Excellent communication and stakeholder-management skills, with the ability to interact effectively with both technical and non-technical stakeholders.
• Ability to act as a Subject Matter Expert (SME) across multiple IS disciplines, including business solutions, systems development, security, and technology architecture.
  

Preferred Profile

• Candidates with strong AI-Native Solution Architecture experience will be highly preferred, particularly those who have hands-on expertise in RAG, LLMs, Agentic AI frameworks, Azure OpenAI, embeddings, and enterprise GenAI solutions.
• The ideal candidate should combine deep Java architecture expertise with modern AI and cloud capabilities and be comfortable driving technical decisions across large, complex enterprise environments.