AI Senior Architect- AI Native Engineering

Daimler AG (Canada); Bengaluru; On-site; INR 4,500,000 - 7,000,000; Full time

Job summary

Mercedes-Benz Research and Development India Private Limited is seeking an AI Senior Architect in AI Native Engineering. You will design end-to-end AI architectures, guide teams, and drive AI adoption across enterprise applications with scalable deployment and MLOps practices.

The role requires 6–9 years IT with 3–4 years in AI/ML, strong full-stack skills, and experience with cloud platforms, Docker, Kubernetes, and CI/CD pipelines. Bengaluru on-site role.

Qualifications

• 9+ years IT experience with 3–4 years in AI/ML or Generative AI.
• Strong full-stack background with enterprise AI design skills.
• Experience in cloud platforms and ML tooling.

Responsibilities

• Design end-to-end AI solutions across data, model, app, and infra layers.
• Define AI platform architecture and deployment strategies.
• Lead AI initiatives and mentor engineering teams.
• Integrate AI into enterprise web apps and APIs.
• Establish MLOps practices and CI/CD for AI systems.
• Collaborate on data pipelines, governance, and security.

Skills

Full stack engineering

Backend development

APIs & microservices

AI/ML technologies

Cloud platforms

Docker & Kubernetes

CI/CD pipelines

AI architecture

Education

Bachelor's Degree in Engineering (CS/IT)

Tools

TensorFlow

PyTorch

Scikit-learn

LangChain

Job description

Job Description - AI Senior Architect- AI Native Engineering (MER0004AAP)

Description

Description - External

Employment Type: Full-Time

Role Overview

We are seeking an AI Architect with 9+ years of overall IT experience, including 3–4 years of hands-on experience in Artificial Intelligence, Machine Learning, or Generative AI solutions. The ideal candidate will have a strong full stack engineering background combined with expertise in designing, developing, and deploying AI-powered systems at scale.

This role involves defining AI architecture strategies, integrating AI capabilities into enterprise applications, enabling intelligent automation, and guiding engineering teams through the AI adoption journey.

Key Responsibilities

• 1. AI Solution Architecture & Strategy
  • - Design end-to-end AI solutions across data, model, application, and infrastructure layers.
  • - Translate business problems into AI-driven technical architectures.
  • - Define AI platform architecture, model deployment strategies, and integration patterns.
  • - Evaluate and select appropriate AI/ML frameworks, tools, and technologies.
  • - Provide architecture guidance for AI initiatives across the organization.
• 2. AI/ML & Generative AI Development
  • - Design and implement machine learning and deep learning solutions where required.
  • - Architect and develop Generative AI applications including LLM integrations, Retrieval-Augmented Generation (RAG), prompt engineering frameworks, and AI agents.
  • - Work with frameworks such as TensorFlow, PyTorch, Scikit-learn, LangChain, or similar tools.
  • - Develop intelligent features within enterprise applications.
• 3. AI Integration with Enterprise Systems
  • - Integrate AI solutions with web applications, APIs, databases, and enterprise platforms.
  • - Design microservices and API-based architectures for AI services.
  • - Enable AI-powered automation across business workflows.
  • - Ensure seamless interaction between AI models and production systems.
• 4. MLOps & Deployment
  • - Define model deployment strategies including real-time inference and batch processing.
  • - Implement MLOps pipelines for model training, versioning, monitoring, and retraining.
  • - Work with containerization and orchestration technologies such as Docker and Kubernetes.
  • - Implement CI/CD pipelines for AI/ML systems.
  • - Monitor model performance, drift, and reliability in production environments.
• 5. Data Engineering & Infrastructure Collaboration
  • - Collaborate with data engineers to design data pipelines and feature engineering workflows.
  • - Define data requirements, storage strategies, and data governance considerations.
  • - Work with cloud platforms such as Azure, AWS, or GCP for AI infrastructure deployment.
  • - Optimize infrastructure for performance and cost efficiency.
• 6. Performance, Security & Governance
  • - Ensure scalability, reliability, and performance of AI solutions.
  • - Implement responsible AI practices including fairness, explainability, and bias mitigation.
  • - Define security and privacy controls for AI systems.
  • - Ensure compliance with data protection and regulatory standards.
• 7. Technical Leadership & Collaboration
  • - Provide technical leadership to engineering and AI teams.
  • - Mentor developers and data scientists on AI best practices.
  • - Participate in architecture reviews and technology decisions.
  • - Collaborate with product managers, business stakeholders, and leadership teams.
  • - Support AI adoption strategy and innovation initiatives.

Required Skills

• - Strong full stack engineering background with experience in backend development (Python, Java, .NET, or Node.js), APIs, and microservices.
• - Hands-on experience with AI/ML technologies including machine learning frameworks and Generative AI/LLM integrations.
• - Experience with cloud platforms (Azure, AWS, or GCP).
• - Experience with Docker, Kubernetes, and CI/CD pipelines.
• - Strong understanding of AI system architecture and deployment patterns.
• - Knowledge of data engineering and MLOps practices.
• - Strong problem-solving and analytical skills.

Preferred Qualifications

• - Experience with enterprise AI platform implementation.
• - Exposure to vector databases (Pinecone, FAISS, Weaviate, etc.).
• - Experience with AI observability and monitoring tools.
• - Knowledge of NLP, computer vision, or recommendation systems.
• - Experience with AI agents or automation frameworks.
• - AI or cloud certifications are a plus.

Education Requirements

• - Bachelor’s Degree in Engineering (Computer Science or Information Technology) is required.
• - Candidates from other educational backgrounds may be considered if they have 100% relevant professional experience.

Experience

• - 6 to 9 years of overall IT experience.
• - Minimum 3 to 4 years of hands-on experience in AI, Machine Learning, or Generative AI solutions.
• - Architectural thinking and system design capability.
• - Innovation and problem-solving mindset.
• - Strong technical leadership and mentoring ability.
• - Communication and stakeholder collaboration skills.
• - Continuous learning orientation.

Organization

Mercedes-Benz Research and Development India Private Limited

Primary Location

India-Karnataka-Bangalore

Work Locations

Brigade Tech Gardens, Katha No. 119 Brigade Tech Gardens, Katha No. 119 Kundalahalli Village, K.R. Puram Hobli, Ward No. 85 Bangalore 560037