Senior AI Engineer – Enterprise Applications
LinkedIn (company site, opens in a new tab)5–8 yrsHyderabadHybridDirect career pageEst. 30–50 LPA
Posted on 15 Sept
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Job description
Structured overview of role & requirements
About This Role
- Design, develop, and deploy production-grade AI agent workflows, RAG pipelines, and platform automations that improve productivity and reduce manual effort across multiple enterprise functions.
- Build and maintain Python-based integrations and automation with Salesforce, ServiceNow, Zendesk, MuleSoft/Workato, and collaboration tools, ensuring scalable, reusable solutions and CI/CD pipeline integration for AI workloads.
- Capture and communicate measurable business impacts, enforce responsible AI practices, and partner with stakeholders to align AI systems with governance, security, and compliance requirements.
Minimum Requirements
- 5–8+ years professional software engineering experience with at least 2–3 years hands-on in AI/ML engineering, LLM application development, or intelligent automation.
- Proficiency in Python, REST API development, Salesforce platform (Flows, Apex, Einstein), and familiarity with CI/CD tools such as GitHub Actions.
- Experience building agentic AI systems using frameworks like LangChain, LangGraph, MCP; expertise in RAG pipeline development and prompt engineering at scale.
- Work Experience Required: 5–8+ years total experience with minimum 2–3 years specific to AI/ML and LLM development. Notice period: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Experienced engineer who has demonstrably shipped mission-critical, production AI systems that deliver quantifiable business impact such as cycle time reduction and error rate improvement.
- Strong operational mindset with proven capability to translate complex business processes into scalable, automated AI-powered workflows and integrations within enterprise SaaS environments.
- Technically skilled in cutting-edge AI frameworks, AI system observability, and responsible AI deployment while collaborating effectively with multi-disciplinary teams including product, architecture, and compliance stakeholders.