# Engineering Manager - Agentic AI **Company**: Telstra **Location**: Bengaluru, Karnataka **Experience**: senior **Job type**: full-time **Category**: Engineering **Industry**: Technology **Apply**: https://telstra.wd3.myworkdayjobs.com/en-US/Telstra_Careers/job/Bengaluru-Karnataka/Engineering-Manager---Agentic-AI_JR-10175693?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_8d16eb23-5d3 ## Description We're an iconic Aussie brand with a global footprint. Our purpose is to build a connected future so everyone can thrive. 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. **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. **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. **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. **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 ## Skills ### Required - Agentic AI architecture - orchestration - LLM - prompt and context engineering - RAG - agentic RAG - enterprise knowledge systems - MCP - agent-to-agent communication - AI evaluation - model selection - token economics - quality gates - responsible AI practices - AI security - governance - privacy - guardrails - risk controls - safe production deployment - Python - AWS - Kubernetes - CI/CD - observability - reliability - operations ### 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 --- Source: [Apply at telstra.wd3.myworkdayjobs.com](https://telstra.wd3.myworkdayjobs.com/en-US/Telstra_Careers/job/Bengaluru-Karnataka/Engineering-Manager---Agentic-AI_JR-10175693?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)