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Tier-1 brand, hybrid role, mid-level experience, and broad AI/enterprise skillset drive high candidate competition.
Role requires specialized LLM/agentic systems and enterprise integration experience, reducing cross-industry transferability.
Explicit 5–8 years plus mandatory LLM, RAG, and enterprise integration skills make screening filters strict.
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.
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.
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.