Project Role : AI Infrastructure Architect
Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration.
Must have skills : Machine Learning (ML)
Good to have skills : NA
Minimum 7.5 Year(s) Of Experience Is Required
Educational Qualification : 15 years full time education
Summary:
As an AI Infrastructure Architect, a typical day involves designing and developing tailored Artificial Intelligence infrastructure and hardware solutions that meet specific organizational needs. This role requires continuous evaluation and enhancement of system performance, power efficiency, cost-effectiveness, and scalability across the computational stack. The professional actively advises on emerging AI infrastructure technologies, assesses vendor offerings, and ensures seamless integration of full-stack solutions to support advanced AI applications. Collaboration with various teams to align infrastructure capabilities with project goals is a key aspect of daily activities, fostering innovation and operational excellence.
Roles & Responsibilities:
- Expected to be an SME, collaborate and manage the team to perform.
- Responsible for team decisions.
- Engage with multiple teams and contribute on key decisions.
- Provide solutions to problems for their immediate team and across multiple teams.
- Lead the evaluation and selection of AI infrastructure technologies and vendors to ensure optimal alignment with organizational objectives.
- Oversee the integration of AI hardware and software components to build scalable and efficient computational environments.
- Mentor junior team members by providing guidance and support to enhance their technical and professional growth.
- Define reference architectures and solution blueprints for multi-agent systems, agent orchestration runtimes, RAG pipelines, and tool-integration layers.
- Select and govern the LLM provider strategy (OpenAI, Anthropic Claude, Google Gemini), vector stores, evaluation harnesses, and responsible-AI guardrails.
- Architect cloud-native services on AWS and GCP (Bedrock, Vertex AI, Cloud Run, Lambda, GKE) with emphasis on scalability, cost, and observability.
- Guide engineering teams in building scalable Node.js backends and Angular experience layers that integrate cleanly with agent endpoints and MCP / AG-UI patterns.
- Establish KPIs, monitoring, CI/CD, and lifecycle-management standards drive code and design reviews.
- Partner with product owners and client stakeholders on roadmap, RFPs, and reusable accelerators.
Professional & Technical Skills:
- Must To Have Skills: Proficiency in Machine Learning (ML).
- Strong knowledge of AI hardware architectures and their impact on machine learning workloads.
- Experience in optimizing computational stacks for performance, power consumption, and cost efficiency.
- Familiarity with vendor evaluation processes and full stack integration of AI infrastructure solutions.
- Ability to design scalable AI infrastructure that supports evolving technology requirements.
- Competence in collaborating with cross-functional teams to align infrastructure design with business and technical goals.
- Must-have skills: Agentic architecture and tools (LangChain, LangGraph, CrewAI, MCP), cloud architecture on AWS and GCP, Node.js, Angular, RAG and LLM integration patterns, CI/CD and observability.
Additional Information:
- The candidate should have minimum 7.5 years of experience in Machine Learning (ML).
- This position is based at our Bengaluru office.
- A 15 years full time education is required.
- 7–10 years in solution or cloud architecture, with at least one production-grade agentic / GenAI system delivered.
