AIOps Platform Engineer Senior Associate Director, Software Engineering

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HSBC is one of the largest banking and financial services organisations in the world, with operations in 64 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people to fulfil their hopes and realise their ambitions.

We are currently seeking an experienced professional to join our team in the role of Senior Associate Director, Software Engineering

We are seeking an experienced Senior AIOps Engineer to design, build, and operate the autonomous agents that turn observability data into safe, closed-loop operational action. You will sit at the intersection of service observability and agentic AI: connecting agents to metrics, logs, traces, events, change data, and service topology so they can detect, diagnose, and remediate production issues with minimal human intervention. You will define how agents reason over telemetry, which tools and runbooks they can call, the guardrails and approval gates that keep their actions safe, and the feedback loops that make them smarter after every incident. This role moves us from dashboards people have to watch to a self-healing production estate where AI observes, decides, and acts — and humans stay in control.

In this role, you will:

- Design and build autonomous AIOps agents (LLM-based and multi-agent) that run the full operational loop — observe, correlate, diagnose, decide, act, and verify — across incident triage, root-cause analysis, and auto-remediation
- Connect agents to the observability estate (OpenTelemetry, Grafana,  Tempo/Jaeger, APM, CMDB, change and deployment events) through well-defined tools/APIs and MCP servers, giving agents real-time, topology-aware context to reason over
- Turn observability signals into autonomous action: build event-driven triggers on SLO burn-rate, anomaly, and correlated alert patterns that launch agent workflows such as rollback, restart, scale-out, failover, traffic shift, config fix, or ticket enrichment
- Build the agent action layer: convert runbooks into machine-executable, idempotent actions (runbook-as-code), with pre-checks, blast-radius limits, dry-run modes, automatic rollback, and post-action health verification against SLIs
- Implement guardrails and human-in-the-loop controls: risk-tiered autonomy levels, policy-as-code, approval gates for high-risk changes, full audit trails, and explainable reasoning for every decision an agent makes
- Apply AI to observability data: anomaly detection, forecasting, alert noise reduction, event correlation, and retrieval-augmented reasoning over past incidents, runbooks, and post-mortems to speed up RCA and cut MTTD/MTTR
- Build evaluation and observability for the agents themselves: trace agent reasoning and tool calls, measure accuracy, action success rate, false-action rate, latency, and cost; run replay/simulation and chaos tests before increasing autonomy in production
- Partner with SRE, platform, and application teams to onboard services to autonomous operations, set standards and reusable patterns, and coach engineers on using AI + observability to automate their own operational workflows

To be successful in this role, you should meet the following requirements:

- 10+ years overall experience with 5+ years in SRE, AIOps, observability, or platform engineering.
- Proven hands-on development of autonomous agents using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, or equivalent; strong grasp of tool/function calling, MCP, planning, memory, and multi-agent orchestration
- Deep service observability expertise: OpenTelemetry, , Grafana, , Jaeger/Tempo, or enterprise APM (Splunk, AppDynamics); strong PromQL/LogQL/SQL and SLO/SLI/error-budget design
- Track record of delivering closed-loop auto-remediation in production that measurably reduced MTTR, alert noise, or manual toil, with clear safety controls and rollback
- Strong Python (Go a plus) with experience building event-driven services and integrations across Kubernetes,  CI/CD, ITSM (ServiceNow), and automation tools.
- Practical knowledge of applied ML and LLM techniques for operations: anomaly detection, time-series forecasting, event correlation, RAG, prompt engineering, and evaluation of LLM outputs
- Understanding of responsible and secure AI in regulated environments: least-privilege agent access, PII redaction, auditability, model risk management, and change-control compliance
- Demonstrated technical leadership, with experience mentoring and leading engineers, setting engineering direction, reviewing designs, and developing team capability while influencing risk partners and senior stakeholders

You’ll achieve more when you join HSBC.

www.hsbc.com/careers

HSBC is an equal opportunity employer committed to building a culture where all employees are valued, respected and opinions count. We take pride in providing a workplace that fosters continuous professional development, flexible working and, opportunities to grow within an inclusive and diverse environment. We encourage applications from all suitably qualified persons irrespective of, but not limited to, their gender or genetic information, sexual orientation, ethnicity, religion, social status, medical care leave requirements, political affiliation, people with disabilities, color, national origin, veteran status, etc., We consider all applications based on merit and suitability to the role.”

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Issued by – HSBC Software Development India