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Protocol Intelligence
Data-driven signals on your job's competitivenessHybrid Bangalore senior AI leadership with broad GenAI demands leads to moderate candidate competition.
Requires deep GenAI, MLOps, and leadership experience, making cross-industry transfers difficult.
Explicit 10+ years, specific GenAI/MLOps platforms, and leadership requirements enforce strict filtering.
Job Description
Structured overview of role & requirementsAbout This Role
Lead and mentor a multidisciplinary AI Engineering and Data Science team delivering GenAI, agentic AI, LLM-integrated applications, and ML models with end-to-end delivery accountability from discovery through production and continuous improvement.
Own architecture, design, and delivery decisions for scalable, secure, and supportable AI/ML systems including LLM-powered systems, retrieval-augmented generation, agent orchestration, and cloud-native services.
Drive engineering discipline and product ownership including SDLC practices, operational readiness, team performance, delivery KPIs, and stakeholder collaboration across product, platform, security, and business teams.
Minimum Requirements
10+ years experience in software engineering or AI/ML engineering with strong production-grade intelligent systems delivery background.
4-5+ years delivering or leading AI/ML/GenAI/LLM-powered solutions in enterprise or product settings.
Proven leadership of multidisciplinary teams responsible for LLM-powered products, agentic AI workflows, and AI-enabled applications with accountability for quality and delivery.
Strong expertise in Python, backend engineering, ML solution delivery, cloud-native application platforms (Azure or equivalent), LLM platforms and orchestration frameworks, RAG implementations, MLOps practices, CI/CD, containerization, and operational tooling.
Ideal Candidate Profile
Technically strong leader capable of guiding senior engineers and data scientists on AI/ML architecture, implementation trade-offs, and engineering excellence in multidisciplinary teams.
Experienced in product-oriented environments requiring end-to-end ownership of complex GenAI and agentic AI systems with cloud-native deployment and scalability considerations.
Skilled at integrating AI/ML solutions with enterprise systems, promoting strong SDLC discipline, and driving continuous improvement of AI product delivery and operational performance.