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The Senior Staff Software Engineer (IC5) on HR Service Delivery sets and owns the technical direction for model-driven capability across the HR domain and the surfaces adjacent domains build on top of it. The work is agentic and conversational experiences that interpret an employee's, manager's, or HR agent's intent, reason over profile, case, catalog, policy, and knowledge context, invoke tools, and act on the user's behalf — but the unit of ownership is the architecture, the standards, and the evaluation infrastructure that let multiple teams ship those experiences safely, not a set of features.
This is not a machine learning or AI research role; the engineer does not train foundation models. It is also distinct from traditional staff-level full-stack engineering, where systems follow deterministic logic rather than selecting execution paths at runtime. First, the most important logic often lives in natural language — instructions, prompts, tool descriptions, guardrails, escalation rules — which must be engineered, versioned, and reviewed with the same discipline as code, and which needs an architecture once more than one team is authoring it. An agent answering on payroll, benefits, leave, or a lifecycle event touches statutory entitlement, jurisdiction-specific policy, and an employee's pay. HR data is among the most sensitive on the platform, and correctness of content is not sufficient. An answer grounded in a record or knowledge article the requestor is not entitled to read is a data exposure even when every fact in it is true, and manager-scope and employee-scope views of the same question have different correct answers. Enforcement belongs in the retrieval and tool layer, not in a request to the model, and at this level you own that being true by construction across every team building in the domain.
What you do Set the AI architecture for the domain
Make the calls with multi-release consequences — model selection and migration, orchestration approach, build-versus-adopt, and the cost, latency, and quality tradeoffs behind each — and own the outcome.
Decide, as a matter of domain policy rather than per-feature design, which HR actions an agent may take, which require a human decision point, and which no agent should attempt. Then build the mechanisms that make those constraints structural and hard to violate accidentally, so that a team shipping a new capability inherits the boundary instead of re-deriving it. You hold the authority to refuse a shipping decision on these grounds, and are expected to use it.
HRSD ships as product: customers configure, extend, and override the instruction and tool surface on their own instances, and adjacent domains build against it. Treat it as a versioned contract with upgrade-safe extension points, deprecation paths, and compatibility guarantees. Own how that surface is structured, reviewed, and evolved across teams — including the authoring standards, the review bar, and the regression coverage that make natural-language logic maintainable at organizational scale rather than only within one codebase.
Build the evaluation infrastructure the organization ships against
Extend coverage to the HR-specific failure classes — access-boundary violations in retrieval and citation,



cross-scope leakage, jurisdictional and policy-variant correctness — and keep it meaningful across the range of customer configurations rather than a single reference environment. Define the resolution, containment, and quality metrics the domain is measured on, be the person who can say whether they are instrumented correctly, and raise the standard of evidence required before a behavioral change ships.
Identify the risks, gaps, and structural weaknesses in model-driven behavior that no one has articulated yet, quantify them, and drive them to a decision. Bring these to product and engineering leadership with the analysis and the recommendation, not the problem alone.
Direct AI coding agents, and set how the organization does
Own production quality, safety, and reliability across the domain
Own the observability and safety posture for agentic behavior: conversation quality, containment, hallucination rate, tool-selection error, unsafe or unauthorized action, and the paths where user-supplied content — case notes, inbound email, attachments, authored knowledge — enters agent context as an injection vector. Because HR conversation content is itself restricted, design diagnosis that works without exposing what was said. Lead root-cause analysis on the incidents no one else can resolve, hold the distinction between a genuine model failure and a platform or configuration failure presenting as one, and close the loop from production failure back into specification and evaluation.
Communicate capability and risk to non-engineering audiences without flattening either. Grow staff-level engineers, raise the bar in technical review and hiring, and make the domain's practices around instruction authoring, evaluation, and accountable agent use durable enough to outlast your involvement in any one project.
Required experience and skills

- Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.
- 12+ years software engineering experience (backend, frontend, or full stack)

Strong JavaScript/Node.js, React and API integration skills
Automated testing (unit + integration) and CI/CD pipeline experience
Solid understanding of data modelling and query optimization
~ Agentic delivery experience:

Hands-on experience with AI/GenAI or ML-driven features (LLM prompting, NLU,

Hands-on experience with LLM-integrated features: prompt design, context injection, output tuning, guardrails.
Experience with NLU/intent classification or conversational AI systems
Familiarity with ML-driven automation (classification, clustering, recommendation

Production AI integration. Experience integrating large language model APIs and retrieval-grounded features, including agent orchestration, tool and function calling, and structured output enforcement.

- Applied machine learning literacy. Accountable use of AI coding agents. Current, effective use of AI coding assistants and agents with evidence of accountable delivery: precise specification,



critical review of generated output, and verification harnesses.
- Operational experience. Hands-on CI/CD, containerized workloads, and observability experience, plus direct on-call and incident-command experience with customer-facing systems.
- Demonstrated mentorship of less-experienced engineers and a record of raising quality through code review.
- Bachelor's degree in computer science, software engineering, or a related technical field, or equivalent practical experience. Advanced degrees are a plus but not a substitute for a record of shipping reliable AI-native applications.

Preferred experience

- HR case management, the employee lifecycle, or payroll, benefits, leave, and absence, at a depth sufficient to challenge a requirement rather than only implement it — including why the same policy question resolves differently by jurisdiction, employment type, or plan year.
- Integration with core HR, payroll, and benefits systems of record, including the reconciliation and eventual-consistency problems that come with treating an external system as the authority on employment data.
- Building capability that customers configure, extend, and override on their own instances, where instruction and tool surfaces are versioned contracts rather than internal implementation.
- Evaluation frameworks, prompt and instruction management tooling, tracing for model-driven applications, and analysis of production transcripts at scale, particularly where transcript content is itself access-restricted.
- Employee self-service portals, virtual agent or chat shells, workplace messaging clients, and voice, including handoff between automated and live agents.
- Working alongside conversation or content designers on dialogue flow, tone, and error-recovery design — with attention to how an HR answer is worded when the subject is the employee's own pay, health coverage, or employment status.
- Building against a customer's data, integrations, and channels, and tuning instructions and evaluation sets in their workplace.
- Scoped applications, ACLs and platform security rules, Flow Designer, UI Builder, Automated Test Framework, and upgrade-safe extension patterns.

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Accommodations
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📌 Senior Staff Front-End Software Engineer (Hyderabad)
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