Sr. Agentic AI/Automation Engineer | Gurugram

DigitalXNode; Gurugram District; On-site; INR 4,500,000 - 9,000,000; Full time

Job summary

DigitalXNode is seeking an experienced Senior Agentic AI / Automation Engineer to design and deploy next‑generation AI-powered enterprise applications using LLMs, agentic frameworks, and RAG. You will lead the development of AI‑driven automation that integrates with enterprise systems and APIs.

You will collaborate with engineers, researchers, and product teams to build secure, scalable cloud‑native AI solutions, with emphasis on governance, observability, and performance optimization.

Qualifications

• Experience building enterprise AI applications with LLMs and agentic architectures.
• Proficiency in designing secure, scalable AI workflows and APIs.
• Strong knowledge of governance, security, and observability for AI systems.

Responsibilities

• Design, develop, and deploy enterprise AI applications using LLMs.
• Build autonomous AI agents capable of planning, reasoning, and executing multi-step workflows.
• Implement Agentic AI architectures using modern orchestration frameworks.
• Develop AI-powered automation solutions that integrate with enterprise applications.
• Collaborate with engineers, researchers, and stakeholders to scale AI deployments.
• Ensure governance, security, and operational excellence of AI systems.

Skills

Generative AI

Agentic AI

LLMs

Prompt Engineering

AI Workflow Orchestration

Multi-Agent Systems

AI Automation

AI Reasoning

Foundation Models

Education

Bachelor's degree in Computer Science / IT / AI / Software Engineering

Tools

OpenAI

Anthropic Claude

Google Gemini

Azure OpenAI

Google Vertex AI (Preferred)

Job description

We are seeking an experienced Senior Agentic AI / Automation Engineer to design, develop, and deploy next-generation AI-powered enterprise applications using Large Language Models (LLMs), Agentic AI frameworks, Retrieval-Augmented Generation (RAG), workflow orchestration, and intelligent automation technologies. This role is ideal for professionals passionate about building autonomous AI agents, enterprise AI platforms, cloud-native applications, and scalable automation solutions.

As a Senior Agentic AI / Automation Engineer, you will collaborate with software engineers, AI researchers, cloud architects, product managers, and enterprise stakeholders to build secure, production-grade AI systems. You will lead the development of AI-driven applications using modern foundation models, cloud platforms, vector databases, and enterprise APIs while ensuring scalability, governance, compliance, and operational excellence.

This opportunity provides hands‑on exposure to Generative AI, Agentic AI, LLM Engineering, AI Workflow Automation, Cloud Infrastructure, Enterprise Integration, and AI Governance, enabling you to work on cutting‑edge enterprise AI transformation initiatives.

Key Responsibilities

Agentic AI & Generative AI Development

• Design, develop, and deploy enterprise AI applications using Large Language Models (LLMs).
• Build autonomous AI agents capable of planning, reasoning, and executing multi‑step workflows.
• Implement Agentic AI architectures using modern orchestration frameworks.
• Develop AI-powered automation solutions that integrate with enterprise applications.
• Build reusable AI services supporting enterprise‑scale automation initiatives.
• Design AI workflows with human‑in‑the‑loop decision‑making capabilities.

LLM Engineering & AI Workflows

• Develop applications using OpenAI, Anthropic Claude, Google Gemini, or similar foundation models.
• Implement Prompt Engineering techniques to improve model accuracy and reliability.
• Design Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge systems.
• Build structured prompting and tool‑calling workflows.
• Fine‑tune AI models where appropriate to improve business outcomes.
• Optimize AI reasoning, response quality, and inference performance.

Enterprise AI Integration

• Integrate AI services with enterprise APIs, internal applications, and business platforms.
• Develop secure RESTful APIs supporting AI‑powered applications.
• Connect AI systems with enterprise data pipelines and knowledge repositories.
• Build scalable backend services supporting AI inference and automation.
• Enable AI‑driven business process automation across enterprise systems.

Cloud & Platform Engineering

• Deploy AI workloads on Google Cloud Platform (GCP), Microsoft Azure, or Kubernetes environments.
• Develop cloud‑native AI applications using Docker and Kubernetes.
• Manage scalable AI infrastructure supporting enterprise workloads.
• Optimize cloud resource utilization and operational efficiency.
• Implement secure deployment strategies following enterprise cloud standards.

Vector Databases & Knowledge Retrieval

• Design enterprise Retrieval-Augmented Generation (RAG) architectures.
• Integrate vector databases such as Pinecone, Weaviate, Elasticsearch, or OpenSearch.
• Build semantic search and enterprise knowledge retrieval systems.
• Improve AI response quality using intelligent document retrieval strategies.
• Optimize indexing, embeddings, and retrieval performance.

AI Performance, Monitoring & Optimization

• Monitor AI application performance, latency, accuracy, and cost efficiency.
• Evaluate LLM outputs using automated evaluation frameworks.
• Implement AI observability and monitoring solutions.
• Optimize prompts, caching strategies, batching, and model selection.
• Detect model drift and continuously improve AI performance.

Security, Governance & Responsible AI

• Implement Responsible AI principles throughout the AI lifecycle.
• Ensure compliance with enterprise security, governance, and regulatory standards.
• Design secure AI applications with encryption, IAM, and access control.
• Support AI risk assessments and governance reviews.
• Maintain secure AI deployment pipelines for regulated enterprise environments.

Leadership & Collaboration

• Lead technical initiatives across enterprise AI engineering projects.
• Mentor engineers in AI engineering best practices and software development.
• Participate in architecture reviews and technical strategy discussions.
• Collaborate with product managers, architects, security teams, and business stakeholders.
• Stay updated with emerging AI technologies, frameworks, and industry best practices.

Required Skills

Artificial Intelligence

• Generative AI
• Agentic AI
• Large Language Models (LLMs)
• Prompt Engineering
• AI Workflow Orchestration
• Multi‑Agent Systems
• AI Automation
• AI Reasoning
• Foundation Models

LLM Platforms

• OpenAI
• Anthropic Claude
• Google Gemini
• Azure OpenAI
• Google Vertex AI (Preferred)

Agent Frameworks

• LangChain
• LangGraph
• CrewAI
• AutoGen
• Semantic Kernel
• OpenAI Agents SDK
• Microsoft AutoGen

Programming

• Python
• REST API Development
• FastAPI
• Flask
• JSON
• Async Programming

Cloud Platforms

• Google Cloud Platform (GCP)
• Microsoft Azure
• Kubernetes
• Docker
• OpenShift

Vector Databases

• Pinecone
• Weaviate
• Elasticsearch
• OpenSearch
• ChromaDB
• FAISS

AI Engineering

• Retrieval‑Augmented Generation (RAG)
• Fine‑Tuning
• Structured Prompting
• Tool Calling
• Function Calling
• Embeddings
• Semantic Search
• Knowledge Retrieval

Software Engineering

• Git
• GitHub
• CI/CD Pipelines
• Software Architecture
• Microservices
• API Integration
• Enterprise Application Development

Security & Governance

• Identity & Access Management (IAM)
• Cloud Security
• AI Governance
• Responsible AI
• Compliance
• Data Security
• Enterprise Risk Management

Monitoring & Observability

• AI Evaluation Frameworks
• Latency Monitoring
• Cost Optimization
• Drift Detection
• Prompt Evaluation
• AI Observability

Professional Skills

• Technical Leadership
• Problem Solving
• Analytical Thinking
• Enterprise Architecture
• Cross‑functional Collaboration
• Stakeholder Management
• Technical Documentation
• Mentoring & Coaching
• Innovation Mindset
• Communication Skills

Preferred Skills

• Power Platform
• Power Apps
• Dataverse
• UiPath
• Enterprise Automation
• AI‑assisted Software Development
• Open Source AI Contributions
• Feature Stores
• Model Registries
• AI MLOps
• Enterprise Data Platforms
• Secure AI Pipelines

Education

Undergraduate

• Bachelor's degree in Computer Science, Information Technology, Artificial Intelligence, Software Engineering, Data Science, or a related technical discipline.
• B.Tech / BE
• BCA (with relevant experience)

Postgraduate (Preferred)

• MCA
• M.Tech
• M.Sc. (Artificial Intelligence / Computer Science / Data Science)
• Master's degree in AI, Machine Learning, or Software Engineering