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
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 across a shared exp
Full 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.
· 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.
Engineering Manager - Agentic AI at Telastra (Bengaluru, Karnataka) — aijobsearch
· 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
· Actively build chapter capability by mentoring engineers in software engineering practices, emerging technologies, tools, approaches and delivery processes, modelling continuous learning and coaching others to lift capability across teams.
· Coach, develop and provide regular feedback to chapter members, clarifying performance, behavioural and engineering expectations, and supporting meaningful growth conversations.
· Build strong commercial capability across teams by modelling disciplined cost-value thinking, balancing engineering investment, platform cost, risk and customer outcomes to choose the most economic course of action.
· Foster a culture of observability across multiple teams, helping engineers use operational telemetry, service health data, incident trends and customer-impact signals to improve stability, performance and domain ownership.
· Influence effective decision-making in uncertain or high-pressure situations, making responsible trade-offs without always having complete information and helping teams manage risk, change and ambiguity.
· Build a strong culture of knowledge sharing, documentation and constructive feedback, using communities of practice, playbooks, reference patterns and learning forums to drive continuous improvement and capability uplift.
· Model Telstra’s values and facilitate inclusive discussions across teams, creating alignment to organisational strategy and principles while identifying actionable continuous improvement opportunities.
· Manage recruitment of permanent employees and partner resources, shaping a healthy workforce plan, succession pipeline and highly productive 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, actively 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.
About YouRequired Skills and Technical Expertise
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.
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.
Preferred Skills
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 across a shared experience.
We're amongst the top 2% of companies globally in the CDP Global Climate Change Index 2023, being awarded an 'A' rating. If you want to work for a company that cares about sustainability, we want to hear from you.
As part of your application with Telstra, you may receive communications from us on +61 440 135 548 (for job applications in Australia) and +1 (623) 400-7726 (for job applications in the Philippines and India).
When you join our team, you become part of a welcoming and inclusive community where everyone is respected, valued and celebrated. We actively seek individuals from various backgrounds, ethnicities, genders and disabilities because we know that diversity not only strengthens our team but also enriches our work. We have zero tolerance for harassment of any kind, and we prioritise creating a workplace culture where everyone is safe and can thrive.
As part of the hiring process, all identified candidates will undergo a background check, and the results will play a role in the final decision regarding your application.
We work flexibly at Telstra. Talk to us about what flexibility means to you. When you apply, you can share your pronouns and / or any reasonable adjustments needed to take part equitably during the recruitment process.
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