Associate Principal Engineer, AI Architect

New

JobgetherSoftware Engineering

IndiaFull-TimeSenior

Salary not disclosed

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Job Details

9+ years of professional experience in software engineering, architecture, or a closely related technical discipline.
Bachelor’s or master’s degree in Computer Science, Information Technology, or a related field.
Strong software engineering expertise with significant hands-on depth in Python.
Proven experience developing microservices and APIs using FastAPI and Node.js for AI and deep learning integrations.
Demonstrated experience architecting and delivering production-grade Generative AI applications at scale.
Strong system-design capabilities across backend, frontend, AI infrastructure, and distributed systems.
Hands-on expertise designing ML, Generative AI, and Agentic AI solutions involving LLMs, RAG, autonomous agents, and AI orchestration.
Experience building end-to-end AI pipelines and integrating vector databases such as Pinecone, Weaviate, or Milvus.
Practical experience deploying secure, production-grade AI solutions across AWS, Azure, and/or GCP.
Strong understanding of CI/CD, access control, AI-assisted development, and cloud-native engineering practices.
Knowledge of AI Governance, Responsible AI, GenAIOps, Knowledge Graphs, and Semantic Layer Architecture.

Translate client business objectives, functional requirements, and technical constraints into scalable, secure, and sustainable AI architectures.
Own the technical vision and architecture for AI-powered applications using technologies such as Python, React, Generative AI, and modern cloud platforms.
Design scalable backend platforms supporting LLM inference, RAG pipelines, agent orchestration, APIs, and microservices.
Architect AI-native frontend experiences, including conversational interfaces, copilots, intelligent dashboards, and other user-facing applications.
Lead the design and implementation of complex GenAI workflows combining LLMs, tools, APIs, structured data, and user context.
Drive AI platformization through reusable components, SDKs, frameworks, and engineering patterns.
Establish standards and best practices for prompt engineering, model integration, evaluation, observability, and production quality.
Define and review non-functional requirements covering scalability, extensibility, security, performance, and reliability.
Lead technical discovery, solutioning, and client or executive-facing workshops for high-impact initiatives.
Mentor engineers and communicate complex architectural and AI concepts clearly to technical and non-technical stakeholders.

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