Support Platform & AI Engineer

Job Description:

ABOUT THE ROLE

The Support Platform & AI Engineer is responsible for designing, operating, and continuously improving the technology ecosystem that powers the client's global Customer Support organisation. This is a specialised engineering role at the intersection of support operations, AI and ML systems, and platform engineering, with a direct mandate to increase the intelligence, efficiency, and automation capability of how the client delivers support to customers worldwide.
These engineers own the AI agent platform, knowledge management system, CRM integrations, automation workflows, monitoring and reporting infrastructure, and all internal support tooling. Working closely with support engineers and leadership, they identify where automation can reduce manual effort, improve response quality, and help achieve the organisation's target of deflecting more than 50% of incoming support requests through AI-powered self-service.

KEY RESPONSIBILITIES

•Own and continuously improve the AI agent platform, including configuration, fine-tuning, knowledge feeding, and performance monitoring of AI-assisted support tools.
•Design and implement support automation workflows that reduce manual effort, accelerate resolution, and improve the customer self-service experience.
•Manage and evolve the knowledge management platform, ensuring content is accurate, structured for AI consumption, and continuously updated.
•Build and maintain CRM and ITSM integrations, reporting dashboards, and operational tooling used by the global support team.
•Monitor platform health, alert on degradation, and proactively identify opportunities to improve system reliability and performance.
•Partner with support engineers to identify automation opportunities from recurring case patterns, translating operational pain points into engineering solutions.
•Track and report on AI deflection rates, knowledge base effectiveness, and platform adoption metrics, using data to drive continuous improvement.
•Evaluate and pilot new support technologies, AI platforms, and automation tools in alignment with the client's technology strategy.
•Ensure data governance, security, and compliance standards are maintained across all support platforms.

REQUIRED QUALIFICATIONS

•3+ years of experience in platform engineering, DevOps, or a technical operations role within a software or SaaS environment.
•Practical experience with AI and ML technologies, including LLM-powered chatbots, retrieval-augmented generation (RAG), or similar AI-assisted support systems.
•Strong proficiency in at least one programming language (Python strongly preferred) for automation and integration development.
•Experience building and maintaining API integrations across enterprise platforms.
•Familiarity with CRM or ITSM platforms (Salesforce, ServiceNow, Zendesk, Jira, or similar).
•Strong data and analytics skills, able to design dashboards and reports that surface meaningful operational insights.
•Excellent collaboration skills, comfortable translating support team requirements into technical solutions.
•AI maturity Level 3: expert-level practitioner with deep hands-on experience building, deploying, and measuring AI systems in production; fluent in prompt engineering, RAG architecture design, LLM observability, and AI governance frameworks.
•Demonstrated ability to architect AI support workflows achieving measurable outcomes: AI deflection rates of 50% or above, draft acceptance rates of 65% or above, first-contact resolution improvement of 20% or above, and agent AI weekly active usage of 85% or above.

PREFERRED QUALIFICATIONS
•Experience building knowledge management systems or AI knowledge pipelines for support or operations use cases.
•Familiarity with vector databases (Pinecone, Weaviate, pgvector), embedding models, and semantic search technologies.
•Experience with cloud platforms (AWS, Azure) and containerised environments (Docker, Kubernetes).
•Knowledge of ITSM process design and service management best practices.
•Experience in a global 24x7 support operations environment.
•Experience implementing AI governance frameworks, including data classification policies, human review checkpoints, model drift monitoring, prompt quality scoring, and responsible AI compliance documentation.
•Proficiency with LLM evaluation frameworks and AI observability tooling (LangSmith, Langfuse, Arize, or similar), with demonstrated experience publishing AI performance metrics to technical leadership and executive audiences.

ABOUT THE COMPANY
A global software company providing geospatial network management solutions to the telecom and utility industries. Its platform helps customers plan, build, and operate complex network infrastructure more efficiently, supporting the digital transformation of networks worldwide.
The company operates internationally and is currently executing a strategic transformation to become a SaaS-first, AI-first business, with the Global Customer Support organisation central to that mission. The Bangalore Centre of Excellence is a new build-out.
The client is an equal opportunity employer, committed to diversity and to creating an inclusive environment for all employees.