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Strong Tier-1 brand, metro location, and sought-after AI role increase candidate competition to medium.
Enterprise-grade ML, MLOps, and governance focus makes cross-industry transferability moderate.
Extensive mandatory production ML, MLOps, cloud, LLM, and governance requirements indicate high shortlisting strictness.
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Design, build, and deploy scalable enterprise AI solutions and LLM-powered applications integrating AI agents, vector databases, and prompt engineering.
Lead productionization of AI systems ensuring high availability, reliability, and maintainability with robust MLOps and AgenticOps practices including CI/CD and automated model lifecycle management.
Collaborate with cross-functional teams to develop reusable AI platforms, enforce AI governance and compliance standards, and drive enterprise AI innovation aligned with business objectives.
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Software Engineering, or related field.
Extensive experience building and deploying production AI/ML systems in enterprise environments.
Proficiency with programming languages such as Python or Java, containerization and orchestration tools like Docker and Kubernetes, and cloud platforms (AWS, Azure, or GCP).
Experience with AI frameworks (PyTorch, TensorFlow, Scikit-learn, LangChain, etc.) and deployment of LLMs, RAG, vector databases, and model governance tools.
Experienced engineer with a strong software engineering foundation and focused expertise in productionizing AI and generative AI solutions at scale in enterprise contexts.
Comfortable working with distributed systems, microservices, event-driven architectures, and cloud-native technologies to build robust AI platforms.
Skilled collaborator who can navigate complex technical and business stakeholder environments to influence AI strategy, architecture, and governance.