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Lead design, build, deployment and support of AI assistants, agentic workflows, and automation. Architect Python microservices, LLM/RAG and vector-search solutions, ensure security, observability, and production reliability, and collaborate across product and engineering teams to operationalize GenAI solutions.
Job Purpose and Impact
Key Accountabilities
Qualifications
- The Senior Software Engineer will lead the design, architecture, build, deployment, and ongoing support of complex AI Assistants, AI Agents, and automation solutions-leveraging ChatGPT-based copilots, Copilot Studio agents, and Power Automate workflows-while adhering to Food APAC AI CoE platform standards and guardrails. Deliverables typically span APIs and integration services, retrieval and vector-search pipelines, enterprise-scale LLMOps/AgentOps operational tooling, and intuitive front-end experiences for office users and plant operators.
This role will work primarily within the Food APAC Digital Technology and Data team, owning and shaping solutions across the Food APAC technology stack that serve internal teams and customers. The Senior Software Engineer will act as a technical leader and trusted partner to the business Product Owner, translating complex product requirements into secure, scalable, observable, and cost-efficient services or agents; managing the lifecycle from experiment to production; and proactively improving reliability, latency, performance, and cost.
- The Senior Software Engineer will lead the design, architecture, build, deployment, and ongoing support of complex AI Assistants, AI Agents, and automation solutions-leveraging ChatGPT-based copilots, Copilot Studio agents, and Power Automate workflows-while adhering to Food APAC AI CoE platform standards and guardrails. Deliverables typically span APIs and integration services, retrieval and vector-search pipelines, enterprise-scale LLMOps/AgentOps operational tooling, and intuitive front-end experiences for office users and plant operators.
Key Accountabilities
- Lead development of Agentic AI workflows, AI assistants, and multi-agent automation solutions.
- Design scalable Python microservices, APIs, LLM, RAG, and vector search architectures.
- Implement prompt engineering, response grounding, caching, and performance optimization strategies.
- Drive testing, debugging, security, reliability, and production support for AI applications.
- Own monitoring, observability, incident management, and operational excellence practices.
- Collaborate with cross-functional teams to deliver business-focused AI solutions.
- Ensure adherence to enterprise architecture, governance, cybersecurity, and Responsible AI standards.
- Strong expertise in Python, cloud-native technologies, APIs, CI/CD, containerization, LLM/RAG frameworks, vector databases, and AI platforms such as Copilot Studio, ChatGPT, Power Automate, Salesforce, and Service Cloud.
Qualifications
- Minimum requirement of 4 years of relevant work experience. Typically reflects 5 years or more of relevant experience.• Typically reflects 4 years or more of relevant experience, including 3+ years working with cloud-native AI/ML or GenAI systems on Azure, AWS, or GCP, or 2+ years of software development experience.
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What you need to know about the Delhi Tech Scene
Delhi, India's capital city, is a place where tradition and progress co-exist. While Old Delhi is known for its rich history and bustling markets, New Delhi is defined by its modern architecture. It's clear the region places a strong emphasis on preserving its cultural heritage while embracing technological advancements, particularly in artificial intelligence, which plays a central role in shaping the city's tech landscape, fueled by investments in research and development.
