Associate Principal Engineer, AI Architect

REQUIREMENTS:

- Total experience 9+ years.

- Should have experience in software engineering, with strong depth in Python.

- Must have experience to develop microservices and APIs (Python, FastAPI, Node.js) to embed AI and deep learning models into enterprise applications.

- Should have proven experience architecting and delivering production-grade Generative AI applications at scale.

- Should have strong system design skills across backend, frontend, and AI infrastructure layers.

- Should be able to architect and implement scalable ML, Generative AI, and Agentic AI solutions (LLMs, RAG, autonomous agents) aligned with business objectives.

- Must have good experience in Enterprise Architecture & Solution Design.

- Should be able to build robust end-to-end AI pipelines and integrate Vector databases (e.g., Pinecone, Weaviate, Milvus) for data processing and feature extraction.

- Must have experience to deploy secure, production-grade hybrid and cloud AI solutions (AWS/GCP/Azure) using modern CI/CD, AI-assisted code optimization, and strict access control.

- Should be able to leverage emerging AI research to continuously tune models, optimize codebases, and manage scalable compute resources.

- Must have experience defining technical strategy and influencing architecture across teams or pods.

- Should be able to mentor junior engineers, drive agile workflows (Jira), and partner with Product managers to define technical strategy.

- Maintain clear technical documentation and translate complex AI concepts for non-technical audiences.

- Should have strong grasp of AI Governance & Responsible AI, Multi-cloud AI platform architecture, AgentOps / GenAIOps, Knowledge Graphs & Semantic Layer Architecture.

- Should have hands-on experience with cloud platforms (AWS, Azure, or GCP) and distributed systems.

- Must have ability to translate ambiguous business problems into durable technical architectures.

- Should have excellent communication skills, with the ability to influence senior stakeholders and technical leadership.

RESPONSIBILITIES:

- Understanding the client’s business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements.

- Own the architecture and technical vision for AI-powered, user-facing applications built with Python, React, and Generative AI.

- Design scalable, secure, and cost-efficient backend platforms for LLM inference, RAG pipelines, and agent-based orchestration.

- Define frontend architecture and UX patterns for AI-native applications, including conversational interfaces, copilots, and intelligent dashboards.

- Lead the design and implementation of complex GenAI workflows that combine LLMs, tools, APIs, structured data, and user context.

- Establish engineering standards and best practices for prompt design, model integration, evaluation, and observability.

- Drive GenAI platformisation—building reusable components, SDKs, and frameworks used across multiple teams or products.

- Partner with product, design, data, and business leaders to translate strategic objectives into scalable technical solutions.

- Review critical designs and codebases, unblock teams on complex technical challenges, and raise the overall engineering bar.

- Lead technical discovery and solutioning for high-impact initiatives, including client or executive-facing workshops when required.

- Ensure enterprise readiness: security, privacy, compliance, governance, and responsible AI practices.

- Use AI-assisted development tools (e.g., Copilot, Claude Code) to accelerate delivery while maintaining production-grade quality.

- Mapping decisions with requirements and be able to translate the same to developers.

- Identifying different solutions and being able to narrow down the best option that meets the client’s requirements.

- Defining guidelines and benchmarks for NFR considerations during project implementation

- Writing and reviewing design do