# Engineering Manager - Agentic AI at Telstra

Telstra is hiring an Engineering Manager - Agentic AI in Bengaluru, India. Level rates it Builds AI ●●●●; you can [apply on Telstra](https://jobsbylevel.com/go/f97071e6-1944-44ff-af9a-3b4c08fdf4f2?utm_medium=ai-agent).

AI Level 4, AI centrality 90 out of 100. Bengaluru, Karnataka.

## Details

- Company: [Telstra](https://jobsbylevel.com/companies/telstra)
- AI level: AI Level 4 (score 90 out of 100)
- Location: Bengaluru, Karnataka
- Posted: October 27, 2026
- Apply: https://jobsbylevel.com/go/f97071e6-1944-44ff-af9a-3b4c08fdf4f2?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=engineering-manager-agentic-ai-at-telstra-851f46

## Prepare for this job

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## Description

Employment Type Permanent Closing Date 6 Nov 2026 11:59pm Job Title Engineering Manager - Agentic AI Job Summary Job Description Who We Are We're an iconic Aussie brand with a global footprint. Our purpose is to build a connected future so everyone can thrive. We're all about providing the best experience and delivering the best tech on the best network. This includes making Telstra the place you want to work. For you, that means a having career that grows with you and working with a team powered by human connection that prioritises wellbeing and choice. What is the focus of the role? The Agentic AI Engineer / Chapter Lead combines hands-on AI engineering depth with chapter leadership. The role designs, builds and operates secure, reliable and cost-effective agentic AI solutions, while developing engineering capability and establishing reusable patterns, standards and reference implementations. The role works across backend, platform, product, architecture, security and SRE teams to move LLM solutions from prototype to governed production use on AWS, with accountability for technical quality, responsible AI and sustainable adoption of AI-assisted engineering practices. What we offer Performance-related pay Access to thousands of learning programs so you can level-up Global presence across 22 countries; opportunities to work where we do business. Up to 26 weeks maternity leave provided to the birth mother with benefits for all child births Up to 16 weeks paid paternity leave provided to the non-birthing partners Employees are entitled to 21 paid annual leaves per calendar year Eligible employees are entitled to 12 days of paid sick / casual leave per calendar year Relocation support options across India, from junior to senior positions within the company Receive insurance benefits such as medical, accidental and life insurances What You’ll Do Agentic AI Solution Engineering · Design, build and operate single-agent and multi-agent systems using LangGraph, Amazon Bedrock Agents and appropriate direct SDK approaches. · Define agent workflows, state, checkpointing, human-in-the-loop controls, tool schemas, contracts, boundaries and safe hand-off patterns. · Apply engineering judgement to determine when agent frameworks are required and when simpler deterministic or direct model integration is better. Retrieval and Enterprise Knowledge Systems · Design and tune RAG and agentic RAG pipelines across approved enterprise knowledge sources, including ingestion, retrieval, indexing, re-ranking and grounding. · Implement hybrid retrieval, query planning, self-correction and tool-augmented retrieval patterns to improve relevance and task completion. · Evaluate vector stores and knowledge graph or GraphRAG approaches where scale, security, cost or relationship-heavy domains require them. MCP, Tools and Agent Communication · Build and operate MCP servers and clients, including tools, resources, prompts and supported transports. · Expose secure capabilities with clear descriptions, validation, versioning, least-privilege access and defined trust boundaries. · Apply agent-to-agent concepts such as discovery, agent cards, task lifecycle, collaboration and capability registration. Evaluation, Quality and Economics · Create evaluation frameworks using golden datasets, deterministic checks, LLM-as-judge techniques, human review and regression suites. · Integrate quality, safety, groundedness, latency, token usage and cost gates into delivery and release processes. · Benchmark models and architectures for quality, reliability, throughput, context requirements and total cost. Security, Governance and Responsible AI · Build security, privacy, safety, guardrails and governance into agentic solutions from inception. · Mitigate risks including prompt injection, data leakage, excessive agency, unsafe tool use, insecure output handling and supply-chain exposure. · Implement PII handling, scoped identity, OAuth where applicable, secret management,

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