Manager - Digital Fabric
Tata Communications · Delhi · onsite · Posted 50d ago
Undisclosed12–20 yrsfulltimeSourced from
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About this role
Role Overview
Key Responsibilities
AI Strategy Architecture
Generative AI Large Language Models
Machine Learning Data Science
Enterprise AI Integration
Cloud Platform Engineering
Data Engineering Knowledge Systems
Leadership Collaboration
Technical Skills
Artificial Intelligence Generative AI
Machine Learning Data Science
Programming Languages
AI Frameworks
Cloud Platforms
Databases Vector Stores
DevOps MLOps
Qualifications
Preferred Experience
Threadspan is seeking a visionary and hands-on AI Architect / Principal AI Engineer to lead the design, development, and deployment of enterprise-grade Artificial Intelligence solutions. The ideal candidate will possess deep expertise in AI/ML technologies, Generative AI, LLMs, Agentic AI frameworks, cloud-native architectures, and enterprise application integration.
This role will drive AI strategy, architect scalable intelligent systems, and collaborate with business stakeholders to transform complex business challenges into innovative AI-powered solutions.
Key Responsibilities
AI Strategy Architecture
- Define and drive the organization''s AI technology roadmap and architecture standards.
- Design scalable, secure, and production-ready AI platforms and solutions.
- Evaluate emerging AI technologies, models, frameworks, and tools for enterprise adoption.
- Provide technical leadership for AI product development and innovation initiatives.
Generative AI Large Language Models
- Architect and implement solutions leveraging:
- OpenAI GPT Models
- Azure OpenAI Services
- Anthropic Claude
- Google Gemini
- Open Source LLMs (Llama, Mistral, Falcon, Phi)
- Design and implement RAG (Retrieval-Augmented Generation) architectures.
- Develop AI Agents and Multi-Agent systems using frameworks such as:
- LangChain
- LangGraph
- CrewAI
- AutoGen
- Semantic Kernel
Machine Learning Data Science
- Design end-to-end Machine Learning pipelines.
- Develop predictive, recommendation, classification, and forecasting models.
- Build MLOps frameworks for model deployment, monitoring, governance, and lifecycle management.
- Establish data quality, model validation, and AI governance practices.
Enterprise AI Integration
- Integrate AI capabilities into ERP, CRM, HRMS, and enterprise applications.
- Design API-driven AI platforms and microservices architectures.
- Develop intelligent automation and workflow orchestration solutions.
- Enable chatbot, copilot, recommendation engine, and document intelligence capabilities.
Cloud Platform Engineering
- Architect AI solutions on:
- Microsoft Azure
- AWS
- Google Cloud Platform
- Design scalable containerized deployments using:
- Docker
- Kubernetes
- Azure AKS / AWS EKS
- Implement distributed computing and vector database architectures.
Data Engineering Knowledge Systems
- Design enterprise knowledge management and semantic search solutions.
- Build AI-powered search platforms using:
- Azure AI Search
- Elasticsearch
- Apache Solr
- Pinecone
- Weaviate
- ChromaDB
- Create data pipelines for structured and unstructured data processing.
Leadership Collaboration
- Partner with business leaders to identify AI transformation opportunities.
- Lead architecture reviews and technical governance forums.
- Mentor AI engineers, software developers, and data scientists.
- Establish AI development best practices, standards, and governance models.
Technical Skills
Artificial Intelligence Generative AI
- Generative AI
- Large Language Models (LLMs)
- AI Agents Multi-Agent Systems
- Prompt Engineering
- Retrieval-Augmented Generation (RAG)
- AI Orchestration Frameworks
- Model Fine-Tuning
- AI Evaluation Frameworks
Machine Learning Data Science
- Machine Learning Algorithms
- Deep Learning
- NLP
- Computer Vision (Preferred)
- MLOps
- Model Monitoring Governance
Programming Languages
- Python (Expert Level)
- Java
- SQL
- JavaScript
- C# (.NET Preferred)
AI Frameworks
- LangChain
- LangGraph
- Semantic Kernel
- LlamaIndex
- CrewAI
- AutoGen
- Hugging Face
Cloud Platforms
- Azure AI Services
- Azure OpenAI
- AWS Bedrock
- Google Vertex AI
Databases Vector Stores
- SQL Server
- PostgreSQL
- MongoDB
- Pinecone
- ChromaDB
- Weaviate
- Elasticsearch
DevOps MLOps
- Jenkins
- GitHub Actions
- Azure DevOps
- Docker
- Kubernetes
- Terraform
Qualifications
- Bachelor''s or Master''s degree in Computer Science, Artificial Intelligence, Data Science, or related field.
- 12-20+ years of software engineering experience with at least 5+ years in AI/ML and Generative AI solutions.
- Proven experience delivering enterprise AI solutions in production environments.
- Strong understanding of enterprise architecture, cloud computing, and data engineering.
Preferred Experience
- AI-powered ERP and enterprise platform modernization.
- Microsoft Copilot, Azure AI Foundry, and Azure OpenAI implementations.
- Conversational AI and enterprise chatbot development.
- Intelligent document processing and knowledge management solutions.
- AI governance, responsible AI, and model risk management frameworks.
Sourced from naukri
Skills
Automationai integrationAI evaluationai developmentai strategyApacheai frameworksai technologiesAksai engineerai governanceAI Product DevelopmentAlgorithmsArchitectureAi SolutionsArtificial IntelligenceAnthropic SDK
About Tata Communications
Tata Communications
Telecom
5.9k+
employees
1986
40 years old
Mumbai, Maharashtra
India
About
Tata Communications facilitates digital transformation for enterprises worldwide, enhancing connectivity, cloud solutions, and operational efficiency through advanced services.
Industries
Telecom
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Culture
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