Senior AI Engineer I
Hybrid · Bengaluru, Karnataka, India
Job Summary
Senior AI Engineer / AI Architect ( Bengaluru ) responsible for designing, building, and operating enterprise-grade AI systems that combine machine learning, generative AI, and scalable cloud-native engineering. Lead end-to-end AI solution design across data strategy, model strategy, integration, evaluation, workflow orchestration, deployment patterns, and monitoring. Build multimodal AI solutions (text, image, tabular), define robust evaluation frameworks, ensure governance and safety, and deliver production-grade AI architectures. Collaborate with software engineers, data engineers, product teams, and stakeholders; mentor junior engineers; drive spec-driven development, AI workflow orchestration, and responsible AI practices. Must have deep expertise in architecture patterns, retrieval-augmented systems, tool-augmented AI, and MLOps, with strong communication and global-team collaboration skills.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or related quantitative discipline
- 10+ years of overall relevant professional experience
- 5+ years of experience in Data Science, AI Engineering, ML Engineering, or AI Architecture roles
- Proven experience designing and delivering production-grade AI systems
- Strong foundation in machine learning, deep learning, statistical modeling, and experimental design
- Practical experience with NLP, computer vision, multimodal AI, generative AI, or reinforcement learning
- Proficiency in Python, R, Java, or equivalent languages
- Strong software architecture and system design skills, including distributed systems and service integration
- Experience with cloud-native AI/data platforms and scalable infrastructure
- Demonstrated ability to define evaluation strategies and quality metrics for AI systems
- Excellent communication, documentation, presentation, and stakeholder management skills
- Ability to work effectively in global, cross-functional, and distributed teams
- Professional certification or advanced training in AI architecture, applied generative AI, or enterprise AI system design
- Experience defining AI reference architectures, deployment patterns, and governance frameworks
- Familiarity with agentic workflows, retrieval systems, tool-augmented AI, model orchestration, and agentic coding practices
- Experience in MLOps, AI observability, CI/CD, model governance, and lifecycle automation
- Strong understanding of prompt engineering, context engineering, retrieval design, and structured output management
- Experience with model benchmarking, red teaming, safety evaluations, and human-in-the-loop review processes
- Ability to evaluate build-vs-buy options and select technologies based on architecture trade-offs
- Experience authoring technical papers, patents, design standards, or architecture guidelines
- Strong mentoring and technical leadership experience
- Demonstrated experience driving teams through structured spec-driven development and AI-assisted engineering practices
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