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 architecture. ⬥ Ability to evaluate technical trade-offs balancing accuracy, latency, scalability, security, cost, and business value. Leadership Experience ⬥ Proven experience leading AI engineering or software engineering teams within a product organization. ⬥ Demonstrated ability to recruit, mentor, and retain high-performing engineering talent. ⬥ Experience managing multiple engineering initiatives while maintaining technical excellence and delivery predictability. ⬥ Strong track record of driving engineering transformation and adoption of emerging AI technologies. Product Mindset ⬥ Deep understanding of the end-to-end AI product lifecycle from ideation, experimentation, and validation through production deployment and continuous optimization. ⬥ Experience working cross-functionally with Product Management, Data Science, Design, and Customer Success teams to deliver customer-centric AI solutions. ⬥ Ability to prioritize engineering investments based on measurable customer and business impact. Operational Skills ⬥ Strong experience with Agile methodologies, sprint planning, engineering execution, and resource management. ⬥ Hands-on knowledge of DevOps, MLOps, CI/CD pipelines, Infrastructure as Code, cloud automation, and AI observability. ⬥ Experience establishing engineering KPIs, operational metrics, and delivery governance for AI products and platforms. Communication ⬥ Excellent written and verbal communication skills with the ability to communicate complex AI concepts to technical and non-technical audiences. ⬥ Ability to influence senior leadership through technical vision, data-driven decision making, and strategic execution. Preferred Qualifications ⬥ Experience delivering enterprise-scale Generative AI products in production. ⬥ Familiarity with foundation models from OpenAI, Anthropic, Google, Meta, or open-source ecosystems. ⬥ Experience with vector databases such as Pinecone, Weaviate, Milvus, pgvector, or similar technologies. ⬥ Exposure to AI evaluation frameworks, LLMOps, AI safety, and responsible AI practices. ⬥ Experience leading platform engineering initiatives supporting AI at scale. ⬥ Master’s degree in computer science, Artificial Intelligence, Machine Learning, or a related field is preferred. Why Join Us? Innovative Environment ⬥ Be part of an organization building next-generation AI-powered products that redefine customer experiences. Career Growth ⬥ Lead strategic AI engineering initiatives with opportunities to shape technology direction and organizational capability. Collaborative Culture ⬥ Work alongside world-class engineers, data scientists, product leaders, and AI practitioners committed to delivering exceptional products. Core Values ⬥ Join a company that lives by the values of shattering boundaries, sparking greatness, and sharing success in everything we do. #LI-Onsite