Engineering Manager - Agentic AI

Telstra Singapore; Bengaluru; Vor Ort; INR 3.000.000 - 6.000.000; Vollzeit

Benefits dieser Stelle

Performance-related pay

Learning programs and training access

Global presence in 22 countries

Maternity & paternity leave benefits

Paid leaves and sick days

Relocation support options in India

Insurance benefits (medical, life)

Zusammenfassung

Telstra is seeking an Engineering Manager for Agentic AI in Bengaluru. The role blends hands-on AI engineering with chapter leadership to design, build and operate secure, cost‑effective agentic AI solutions across backend, platform, product, architecture, security and SRE teams.

You will govern production use of LLM-powered systems on AWS with a focus on responsible AI and scalable adoption. You will drive technical quality, patterns, and reference implementations while coordinating with

Aufgaben

• 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.
• 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.
• 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.
• 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.
• 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, least-privilege access, human approval and audit trails for sensitive actions.
• Apply data residency, retention, privacy, versioning, hallucination and bias risk assessment, change control and responsible AI requirements.
• Build AI services using strong Python practices, including asynchronous programming, FastAPI, automated testing and maintainable package design.
• Deploy and operate services on AWS and Kubernetes with CI/CD, secure runtime configuration and operational controls.
• Instrument agent systems with tracing, logging, metrics and evaluation telemetry using OpenTelemetry and relevant LLM observability platforms.
• Implement reliability patterns including timeouts, retries, fallbacks, failover, rate limits, concurrency controls and graceful degradation.
• Treat prompts, agent definitions, evaluation datasets, policies and configuration as versioned, reviewed and tested engineering artefacts.

Kenntnisse

AI engineering

Chapter leadership

Python

Full-stack collaboration

Tools

LangGraph

Amazon Bedrock Agents

FastAPI

AWS

Kubernetes

OpenTelemetry

Jobbeschreibung

Employment Type

Permanent

Closing Date

6 Nov 2026 11:59pm

Job Title

Engineering Manager - Agentic AI

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, least-privilege access, human approval and audit trails for sensitive actions.
• Apply data residency, retention, privacy, versioning, hallucination and bias risk assessment, change control and responsible AI requirements.

Production Engineering and Operations

• Build AI services using strong Python practices, including asynchronous programming, FastAPI, automated testing and maintainable package design.
• Deploy and operate services on AWS and Kubernetes with CI/CD, secure runtime configuration and operational controls.
• Instrument agent systems with tracing, logging, metrics and evaluation telemetry using OpenTelemetry and relevant LLM observability platforms.
• Implement reliability patterns including timeouts, retries, fallbacks, failover, rate limits, concurrency controls and graceful degradation.
• Treat prompts, agent definitions, evaluation datasets, policies and configuration as versioned, reviewed and tested engineering artefacts.

Chapter Leadership and Capability Uplift

• Activ