Gen AI Architect

Ericsson GmbH; Kolkata District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Ericsson GmbH is seeking an experienced GenAI/AI Architect to lead the design and deployment of enterprise AI solutions in India. You will own the architecture journey from strategy to production, translating AI capabilities into measurable business value.

You will design end-to-end architectures, evaluate models and tools, and guide teams across data science, security, and operations to ensure scalable, compliant deployments.

Qualifications

• Bachelor's or master's degree in computer science, statistics, engineering, or related field.
• Demonstrated track record architecting large-scale AI/ML systems in production environments.
• Deep expertise in Python and ML frameworks (PyTorch, TensorFlow) and DL practices.

Responsibilities

• Design end-to-end architectures for production-grade GenAI/AI systems with reference architectures and reusable patterns.
• Evaluate LLMs, vector databases, and orchestration frameworks for performance, cost, and security.
• Architect retrieval-augmented generation (RAG) pipelines and multi-agent workflows for scalable deployment.
• Collaborate with data scientists, security, developers, and business leaders to align technical solutions with needs.

Skills

Python

PyTorch

TensorFlow

LLM architectures

RAG pipelines

Vector databases

Docker

Kubernetes

MLOps

Communication

Education

Bachelor's or Master's in CS/Engineering

Tools

AWS

Azure

Job description

Join our Team

About this opportunity:

We're seeking an experienced GenAI/AI Architect to lead the design and implementation of enterprise-scale artificial intelligence solutions. You'll own the complete architecture journey—from strategy through production deployment—working across technical and business teams to translate AI capabilities into measurable business value. This role is ideal for someone who combines deep technical expertise with the ability to navigate complex organizational landscapes and emerging technology trends.

What you will do:

• Design end-to-end architectures for production-grade GenAI/AI systems, establishing reference architectures and reusable patterns for enterprise adoption
• Evaluate and recommend large language models, vector databases, and orchestration frameworks based on performance, cost, compliance, and security requirements
• Architect retrieval-augmented generation (RAG) pipelines, agentic workflows, and multi-agent systems for scalable deployment
• Collaborate with data scientists, security teams, developers, and business leaders to align technical solutions with organizational needs
• Embed governance, privacy, and ethical frameworks into AI systems from design through deployment
• Guide teams on model lifecycle management, including deployment strategies, monitoring, performance optimization, and handling model drift
• Evaluate tools and frameworks for system integration and interoperability
• Drive adoption of cloud-native AI services and optimize for scalability and performance
• Stay informed on GenAI/AI advancements and regulations, translating insights into actionable strategic roadmaps

The skills you bring:

• Bachelor's or master's degree in computer science, statistics, engineering, or related field
• Proven track record architecting large-scale AI/ML systems in production environments
• Deep expertise in Python, machine learning frameworks (PyTorch, TensorFlow), and deep learning practices
• Strong knowledge of large language model architectures, fine-tuning techniques, and deployment strategies
• Hands‑on experience with RAG pipelines, embeddings, and vector databases
• Familiarity with agentic GenAI frameworks and integration protocols
• Cloud architecture experience (AWS, Azure) and container orchestration (Docker/Kubernetes)
• Solid understanding of MLOps principles: CI/CD pipelines, observability, model governance, and retraining workflows
• Ability to communicate complex AI strategies effectively to technical and non-technical stakeholders
• Demonstrated commitment to responsible AI, including governance and ethical considerations
• Bonus: Experience scaling AI solutions in telecom or similar complex enterprise environments