Job Overview

Cisco is hiring a Software Engineer specializing in Agentic AI, Large Language Models, and Python for its Product and Engineering organization in Bangalore. This is a hybrid opportunity for an experienced engineer who can combine backend engineering, machine learning, cloud architecture, data pipelines, and modern AI application development.

The role focuses on building scalable and production-ready AI systems with an emphasis on Agentic AI, LLM applications, LangGraph, and Retrieval-Augmented Generation architecture. You will contribute to technical architecture, backend services, distributed systems, cloud-native platforms, and AI infrastructure while helping engineering teams deliver reliable solutions.

A major part of the position involves turning advanced AI concepts into dependable production systems. This includes improving LLM performance, controlling token usage and latency, optimizing infrastructure costs, designing safeguards, and establishing effective evaluation and observability practices. The role also involves close collaboration with product, design, engineering, and business stakeholders.

Key Responsibilities

• Design and develop scalable Python backend systems that support AI and data-intensive workloads.
• Architect and build Agentic AI and LLM applications using LangGraph and related technologies.
• Develop RAG architectures and establish reusable standards for AI services, APIs, databases, and distributed systems.
• Define practical strategies for improving LLM throughput, latency, token consumption, reliability, and operating cost.
• Design and maintain data pipelines while improving ETL workloads through scheduling, partition pruning, caching, and storage-format optimization.
• Contribute to AWS cloud-native architecture using services and technologies such as S3, SQS, SNS, Lambda, Docker, and Kubernetes.
• Support the deployment and operational management of deep learning and LLM models in production environments.
• Implement approaches for versioned prompts, runtime safeguards, and rollback strategies for LangGraph and multi-agent workloads.
• Use LLM evaluation and observability solutions to understand application quality and production behavior.
• Investigate and resolve complex development and production issues across multiple engineering teams.
• Prepare technical design documentation and contribute to documentation that helps users and engineering teams understand the solutions.
• Work with product managers, designers, and other technical groups to translate customer and business needs into scalable technology solutions.
• Mentor engineers and contribute to engineering standards, architecture decisions, and technical direction.
• Identify opportunities for product innovation and improvements to software development practices.

Required Skills

Strong professional experience in machine learning engineering, backend development, and applied AI is required. Candidates should have deep knowledge of Agentic AI, LLM applications, LangGraph, and RAG architectures.

Strong Python expertise is essential, along with practical knowledge of backend engineering and distributed systems. Candidates should be comfortable designing systems that need to operate reliably at scale and should understand the engineering considerations involved in production AI applications.

The role requires experience operationalizing deep learning and LLM models in production. You should also understand how to deploy LangGraph and multi-agent workloads with appropriate prompt versioning, runtime controls, and rollback mechanisms.

Candidates should be able to analyze and improve LLM systems using measurable performance factors such as token consumption, response latency, throughput, and cost. Experience with LLM evaluation and observability tooling, such as LangSmith or an equivalent platform, is valuable for this work.

Preferred Skills

Experience mentoring engineers and leading large, cross-functional technical initiatives is preferred. The role also values candidates who can connect technical architecture with business objectives and stakeholder priorities.

Knowledge of enterprise platform integration patterns and network technologies can be useful. Strong technical communication is important, particularly the ability to explain complex architecture and engineering decisions clearly to senior stakeholders.

Experience with Model Context Protocol and related AI tool ecosystems is also preferred.

Education

The supplied job description does not specify a formal educational qualification. Candidates should therefore be evaluated primarily against the stated technical experience and skills rather than an assumed degree requirement.

Experience

The role requires 5 to 10 years of hands-on experience in machine learning engineering, backend development, and applied AI according to the detailed minimum qualifications. The job title references a 5 to 8 year experience range, while the qualification section states 5 to 10 years. The broader qualification range is represented here because it is explicitly provided in the job requirements.

Required Technologies

• Python
• Agentic AI
• Large Language Models (LLMs)
• LangGraph
• Retrieval-Augmented Generation (RAG)
• Machine Learning
• Deep Learning
• Backend Development
• Distributed Systems
• ETL
• AWS
• Amazon S3
• Amazon SQS
• Amazon SNS
• AWS Lambda
• Docker
• Kubernetes
• APIs
• Databases
• LangSmith or equivalent LLM evaluation and observability tooling
• Multi-agent systems
• Versioned prompts
• Runtime safeguards
• Rollback strategies
• Model Context Protocol (MCP)

Soft Skills

This position requires strong analytical thinking, ownership, and the ability to solve complex technical problems. Because the engineer will work across product, engineering, design, and business groups, clear written and verbal communication is important.

A successful candidate should be comfortable influencing technical direction, explaining architecture to different audiences, mentoring colleagues, and working through ambiguous requirements. The ability to connect engineering decisions with customer and business outcomes is also important.

Benefits of Working in this Role

This role offers exposure to advanced AI engineering across Agentic AI, LLM applications, RAG, distributed systems, data engineering, and cloud-native architecture. It provides an opportunity to work on production AI systems rather than limiting the work to experimentation or prototypes.

The position also offers technical leadership opportunities through architecture decisions, engineering standards, mentoring, cross-functional initiatives, and production problem solving. These responsibilities can help experienced engineers broaden their expertise across AI platforms and enterprise software engineering.

Work Mode

Hybrid.

Location

Bangalore, Karnataka, India.

Who Should Apply

This opportunity is intended for experienced software and AI engineers with at least 5 years of relevant hands-on experience. It is particularly suitable for professionals who have built backend systems in Python and have practical experience taking LLM, deep learning, or Agentic AI solutions into production.

Candidates with experience in LangGraph, RAG, multi-agent workloads, LLM evaluation, AWS, Kubernetes, data pipelines, and distributed systems should consider this role. Engineers who enjoy architecture, technical leadership, mentoring, and solving complex production challenges may also be a strong fit.

Career Growth

The role can support career development toward senior AI engineering, AI platform architecture, backend architecture, machine learning engineering leadership, and technical leadership positions. Experience with production LLM systems, cloud-native architecture, distributed systems, and cross-functional technical strategy can strengthen a professional's ability to lead complex AI initiatives.

Application Advice

Tailor your resume toward production-focused AI and backend engineering experience. Highlight specific work involving Python, LLM applications, Agentic AI, LangGraph, RAG, deep learning, distributed systems, and cloud platforms.

Clearly describe any experience improving token efficiency, latency, throughput, reliability, or AI infrastructure costs. Include examples of production deployments, multi-agent systems, prompt versioning, safeguards, rollback strategies, evaluation, and observability where applicable.

Also highlight AWS services, Docker, Kubernetes, ETL optimization, data pipelines, mentoring, architecture ownership, and cross-functional leadership. If you have worked with Model Context Protocol or related AI tool ecosystems, make that experience visible in your application.