Noida Sector 45 Noida Uttar Pradesh Devops engineer sql+automation Jobs – 85 Open Jobs Today

Manager - Digital Fabric
Tata Communications —Delhi, Delhi
- Full-time
- Azure
- Computer Science
- ERP systems
11d
Microsoft Biz Apps – Software Engineer Manager
Crowe Capability Center - India —Noida, Uttar Pradesh
- Full-time
- Azure
- DevOps
- English
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Manager - Digital Fabric

Tata Communications -
Delhi, Delhi

Apply Now

Job details

Full-time
11 days ago

Qualifications

Knowledge management
Elasticsearch
Image processing
Azure
Cloud architecture
Computer Science
Kubernetes
Computer vision
C#
ERP systems
Microsoft SQL Server
Git
Google Cloud Platform
MongoDB
Enterprise architecture
.NET
Java
Master's degree
SQL
OS Kernels
AWS
Docker
Bachelor's degree
Machine learning
JavaScript
PostgreSQL
Distributed systems
Terraform
Deep learning
Natural language processing
GitHub
APIs
Cloud computing
Data science
Solr
AI
Jenkins
Python
Data Science

Full job description

Tata Communications Redefines Connectivity with Innovation and IntelligenceDriving the next level of intelligence powered by Cloud, Mobility, Internet of Things, Collaboration, Security, Media services and Network services, we at Tata Communications are envisaging a New World of Communications

Role Overview

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.

Apply Now

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Microsoft Biz Apps – Software Engineer Manager

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Role Overview

Key Responsibilities

AI Strategy & Architecture

Generative AI & Large Language Models

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

Preferred Experience

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.

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.

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.

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.

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.

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 Algorithms
Deep Learning
NLP
Computer Vision (Preferred)
MLOps
Model Monitoring & Governance

Python (Expert Level)
Java
SQL
JavaScript
C# (.NET Preferred)

LangChain
LangGraph
Semantic Kernel
LlamaIndex
CrewAI
AutoGen
Hugging Face

Azure AI Services
Azure OpenAI
AWS Bedrock
Google Vertex AI

SQL Server
PostgreSQL
MongoDB
Pinecone
ChromaDB
Weaviate
Elasticsearch

Jenkins
GitHub Actions
Azure DevOps
Docker
Kubernetes
Terraform