Senior Manager, AI Engineering

We're looking for a Senior Manager, AI Engineering This role is Office Based, Hyderabad Office Job Description Job Title : Senior Manager AI Engineering Location : Hyderabad Years of Experience : 10+ years in software engineering and AI/ML development, with 4+ years in engineering leadership or management roles. Position Overview We are seeking an experienced and visionary Senior Manager AI Engineering to lead high-performing AI engineering teams responsible for building, deploying, and scaling AI-powered products and platforms. This leader will drive the engineering execution of Generative AI, LLM-based applications, AI platforms, intelligent automation, and machine learning solutions that directly power customer experiences and business outcomes. The ideal candidate combines strong engineering fundamentals with hands-on expertise in modern AI technologies, cloud-native architectures, and large-scale distributed systems. They should have proven experience building production-grade AI solutions while developing high-performing engineering teams in a product-driven organization. In this role you will. Team Leadership and Development Lead, mentor, and grow a team of AI engineers, machine learning engineers, and software engineers. Foster a culture of innovation, experimentation, engineering excellence, and continuous learning. Build organizational capability in Generative AI, LLM engineering, AI platforms, and modern software engineering practices. Drive technical leadership, career development, and succession planning across the organization. AI Engineering & Technical Leadership Drive the architecture, design, and development of scalable AI-powered products using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and machine learning solutions. Lead engineering efforts across Python, cloud-native technologies, APIs, vector databases, orchestration frameworks, and distributed systems. Define AI engineering standards, reusable frameworks, governance, and best practices for secure and reliable AI application development. Conduct architecture, design, and code reviews to ensure quality, scalability, maintainability, and security. Product Delivery Partner closely with Product Management, Data Science, UX, and Platform Engineering teams to define AI product roadmaps and technical strategy. Translate business opportunities into scalable AI capabilities and production-ready solutions. Lead sprint planning, technical execution, prioritization, and resource allocation to deliver high-quality AI products on schedule. Drive rapid experimentation while ensuring production readiness and long-term maintainability. AI Platform & Operational Excellence Build and scale enterprise AI platforms supporting model serving, prompt management, RAG pipelines, AI observability, evaluation frameworks, and MLOps. Implement engineering best practices including CI/CD, Infrastructure as Code, automated testing, monitoring, and AI model lifecycle management. Ensure AI systems are secure, reliable, scalable, cost-efficient, and compliant with responsible AI principles. Establish engineering metrics around model quality, latency, cost optimization, reliability, and customer impact. Stakeholder Engagement Serve as the bridge between engineering leadership, product teams, executive stakeholders, and business partners. Communicate technical strategy, architecture decisions, delivery progress, risks, and business outcomes. Influence AI adoption across the organization by aligning engineering initiatives with strategic business priorities. You've got what it takes if you have. AI & Technical Expertise Strong hands-on experience developing production-grade AI applications using Java, Python and modern AI frameworks. Deep expertise with Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), prompt engineering, AI agents, embeddings, and vector databases. Experience integrating commercial and open-source foundation models through APIs and model serving platforms. Strong understanding of AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar technologies. Experience designing scalable microservices, REST APIs, event-driven architectures, and cloud-native AI applications. Expertise with cloud platforms such as AWS, Azure, or Google Cloud for deploying AI workloads. Experience building AI platforms leveraging services such as Kubernetes, Docker, serverless technologies, model hosting platforms, and GPU-enabled infrastructure. Practical experience with MLOps practices including model deployment, versioning, monitoring, experimentation, evaluation, and continuous improvement. Strong knowledge of SQL, NoSQL databases, vector databases, caching strategies, and performance optimization for AI workloads. Understanding of AI governance, model security, privacy, responsible AI, and enterprise AI We're loo