Senior Ai Platform Engineer

and continually improvereliability, latency, and cost. Typical deliverables span REST services, vector-search back end, LLMOps & AgentOps pipelines, and rich front-end components for knowledge workers and plant operators. Expect to collaborate daily with product managers, data scientists, cloud engineers, and security teams while acting as the technical authority for GenAI feature design and launch.

Key Accountabilities

SOFTWARE DEVELOPMENT : Designs and develops high quality software solutions by writing clean, maintainable and efficient codes.

AUTOMATION : Leads the application of internal software deployment platform, methodologies and tools to automate the deployment process, ensuring smooth and reliable releases.

COLLABORATION : Partners with cross functional team of product managers, designers and different engineers to gather complex requirements and deliver solutions that meet business needs.

TESTING & DEBUGGING : Writes and maintains complex unit tests and integration tests, and performs debugging to maintain the quality and performance of the software.

CONTINUOUS IMPROVEMENT : Suggests options for improving the software development and deployment processes, and implements the approved standards to improve efficiency and reliability.

DOCUMENTATION : Builds and maintains comprehensive documentation for complex software applications, deployment processes and system configurations.

TECHNICAL SUPPORT : Provides technical support and troubleshooting for complex issues with deployed applications to ensure minimal downtime and fast resolution.

ESSENTIAL FUNCTIONS :

Design & Build

Develop multi-agent workflow automation patterns using Agentic AI

Process redesign and mapping to agentic workflow patterns

Architect scalable micro-services that wrap LLM / RAG / Agent workflows (Python).

Implement robust prompt-engineering patterns, retrieval pipelines, and caching for AI Assistants and AI Agents

Platform Ops

Extend evaluation, automated testing, canary rollout, and rollback for AgentOps.

• Profile inference latency, GPU / CPU utilization, and memory;

deliver quarterly cost-to-serve reductions

Lead bug-fix, security-patch, and performance-tuning sprints for live AI Assistants and AI Agents

Operational Excellence

• Own on-call runbooks, SLOs, and incident reviews;

embed observability

Enforce Responsible-AI guardrails, data-privacy controls, and vulnerability-management policies for AI Assistants and AI agents

Enablement & Mentoring

• Coach full-stack and data-science peers on GenAI / LLMOps patterns;

create internal workshops and tech blogs.

Qualifications

Minimum : 4 years building production software or data platforms .

Typical : 5–8 years, including 2+ years with cloud-native AI / ML or GenAI systems (Azure, AWS, or GCP) or 2+ years of software devlopment