18 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Architect, build, deploy, and operate production-grade AI systems and agentic workflows across the SDLC and business processes.
- Design scalable internal platform capabilities, reusable services, infrastructure, patterns, and guardrails for AI-powered automation.
- Own solutions from problem definition and architecture through implementation, deployment, observability, security, scaling, and ongoing operation.
- Integrate AI models and agents with developer tooling, internal platforms, enterprise systems, APIs, data sources, and engineering workflows.
- Apply software engineering principles including modular architecture, testing, reliability, performance, maintainability, and fault tolerance.
- Design AI evaluation, observability, quality measurement, failure handling, and continuous-improvement approaches.
- Design and operate secure, reliable, scalable cloud-native workloads using GCP and/or AWS.
- Partner with infrastructure, DevOps, security, architecture, and engineering teams on technical standards and operational guardrails.
- Identify opportunities to improve developer workflows across the end-to-end SDLC and evaluate emerging AI models, frameworks, agent architectures, and developer technologies.
- Provide Staff-level technical leadership through architecture reviews, design decisions, mentorship, and cross-team influence.
- Translate ambiguous business and engineering problems into technical strategies and measure impact using production data, user feedback, and business results.
Requirements
- 10+ years of professional software engineering experience designing, building, and operating complex production-scale systems.
- Hands-on experience designing and building AI/ML or generative AI solutions that reached production or delivered measurable business outcomes.
- Strong software architecture, system design, distributed-systems, and large-scale engineering experience.
- Strong programming fundamentals in one or more production languages such as Java, Python, or Go.
- Hands-on experience with LLMs, generative AI, AI agents, or AI-enabled application architectures beyond simply using AI productivity tools.
- Experience taking AI solutions from prototypes through deployment, operationalization, monitoring, security, reliability, and scale.
- Deep hands-on experience designing and operating cloud-native production systems with GCP and/or AWS.
- Strong understanding of DevOps, infrastructure as code, observability, reliability, production operations, application security, infrastructure security, data security, and AI security.
- Strong understanding of the software development lifecycle, developer workflows, and large software systems and codebases.
- Ability to evaluate technical trade-offs, make pragmatic technology decisions, operate in ambiguity, and influence technical direction across teams.
- Preferred experience with agentic AI systems, multi-step workflows, tool-using agents, autonomous or semi-autonomous engineering workflows, and AI applications for developer productivity.
- Preferred experience with internal developer platforms, engineering productivity platforms, enterprise automation, AI evaluation frameworks, model or agent observability, prompt and context management, retrieval architectures, and production AI quality measurement.
- Preferred experience spanning traditional software engineering and AI engineering, and driving initiatives across multiple teams, systems, or organizational domains.
Benefits
- Onsite Bengaluru work arrangement with an expectation of four office days per week and potential increase to five days as required by the business.
- Medical insurance for employees, spouses, dependent children, parents, or in-laws, subject to stated coverage limits.
- Group term life and group personal accident insurance.
- 15 privilege leave days, 6 paid sick leave days, 6 casual leave days, paid maternity and paternity leave, a birthday day off, and paid holidays.
- Provident Fund and gratuity.
- Employee Assistance Program and wellness initiatives.
- Learning and development opportunities and career advancement.
Categories
