Engineering Manager - Agentic AI

Overview

Lead the Agentic AI chapter at Telstra, combining hands-on engineering of secure, reliable, cost-effective agentic AI solutions with chapter leadership, mentoring and capability uplift.

What you'll do

• 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.
• Mentor engineers in software engineering practices, emerging technologies, tools, approaches and delivery processes, and coach others to lift capability across teams.
• Coach, develop and provide regular feedback to chapter members, clarifying performance, behavioural and engineering expectations, and supporting growth conversations.
• Build commercial capability by modelling disciplined cost-value thinking, balancing engineering investment, platform cost, risk and customer outcomes.
• Foster a culture of observability across multiple teams, using operational telemetry, service health data, incident trends and customer-impact signals to improve stability, performance and domain ownership.
• Influence decision-making in uncertain or high-pressure situations, making responsible trade-offs and helping teams manage risk, change and ambiguity.
• Build a culture of knowledge sharing, documentation and constructive feedback through communities of practice, playbooks, reference patterns and learning forums.
• Model Telstra's values and facilitate inclusive discussions across teams, creating alignment to organisational strategy and principles.
• Manage recruitment of permanent employees and partner resources, shaping workforce plan, succession pipeline and team mix aligned to current and future capability needs.
• Set goals and lead performance appraisal for engineers, ensuring expectations, outcomes, development needs and accountability are clearly understood and fairly assessed.
• Ensure co-workers are treated respectfully and without discrimination, harassment or victimisation, contributing to psychological safety, inclusion and a respectful workplace.
• Assess AI and modern engineering capability needs, align people to priority work, and support safe adoption of AI-assisted engineering tools, spec-driven development and reusable standards.
• Balance hands-on technical leadership with people accountability, chapter health, resource planning and delivery commitments.

What you'll need

• Agentic AI architecture, orchestration, LLM, prompt and context engineering.
• RAG, agentic RAG, enterprise knowledge systems, MCP and agent-to-agent communication.
• AI evaluation, model selection, token economics, quality gates and responsible AI practices.
• AI security, governance, privacy, guardrails, risk controls and safe production deployment.
• Strong production engineering across Python, AWS, Kubernetes, CI/CD, observability, reliability and operations.
• Technical leadership, stakeholder influence, coaching, workforce capability uplift and reusable engineering standards.
• 10+ years professional software engineering experience, including 2+ years building LLM-based applications or equivalent production AI depth.
• Experience taking agentic or generative AI solutions from discovery and prototype to production operation.
• Experience leading technical outcomes across teams and coaching engineers in emerging technologies.
• Degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science or related discipline, or equivalent relevant experience.

Nice to have

• Integration of AI capabilities with Java, Spring Boot or Node.js services using Spring AI, LangChain4j or comparable libraries.
• Fine-tuning or parameter-efficient adaptation, including LoRA.
• Experience with additional agent frameworks such as Strands Agents, CrewAI, OpenAI Agents SDK or Claude Agent SDK.
• Background in SRE, platform engineering, event-driven architecture, Kafka or Redis Streams.
• Contributions to AI tooling, internal engineering communities or relevant cloud and AI certifications.
• Experience with enterprise model gateways, agent registries, AI inventory, governance platforms or internally hosted model ecosystems.
• End-to-end solution design from chat UI through orchestration, retrieval, tool execution and LLM response delivery, including clear ownership of user experience, backend services and model interaction patterns.
• Understanding of A2UI, A2A and shared UI communication practices, including how agents, user interfaces and backend services coordinate context, actions, state and hand-offs safely.

Details

• Employment type: Permanent.
• Closing date: 6 Nov 2026, 11:59pm.
• Work flexibly; flexibility to be discussed with the hiring team.