Tech S and T – AI Engineer, Manager

Jobtailor; Dadri; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Jobtailor seeks an experienced infra AI architect to lead the design and delivery of AI/ML/LLM-powered automation for infrastructure operations. You will build agentic workflows, integrate with cloud platforms and ITSM tools, and guide governance and cost controls.

You will mentor engineers, drive MLOps practices, and oversee the end-to-end lifecycle from POC to production for AI initiatives in a large enterprise context.

Qualifications

• 10–12 years in Infrastructure/Cloud engineering or architecture.
• Last 3–4 years focused on applied AI/ML/LLM work.
• Strong hands-on coding in Python; scripting in Bash/PowerShell.
• API integration with OpenAI, Anthropic, LangChain, LlamaIndex.
• Understanding LLM prompting, embeddings, RAG, and token costs.
• Experience building agentic AI systems with orchestration tools.

Responsibilities

• Architect and deliver AI/ML/LLM-based solutions for infra operations.
• Design agentic AI workflows for automation, provisioning, and remediation.
• Integrate AI with cloud and enterprise tools (Azure/AWS/GCP, ServiceNow, Datadog, Splunk).
• Define AI governance guardrails for data privacy and cost management.
• Lead POC-to-production lifecycle for AI initiatives and MLOps practices.
• Mentor engineers and act as AI Center of Excellence lead.

Skills

Python Coding

Bash Scripting

PowerShell Scripting

API Integration

LLM Fundamentals

Agentic AI Systems

Vector Databases

Cloud Architecture

MLOps/LLMOps

Automation

Tools

LangChain

LlamaIndex

OpenAI

Anthropic

Pinecone

Weaviate

FAISS

Azure AI Search

Terraform

Ansible

Job description

• Architect and deliver AI/ML/LLM-based solutions embedded into infrastructure operations, including ITOps/AIOps, self-healing systems, predictive capacity, and automated RCA
• Design and build agentic AI workflows for infrastructure automation, including ticketing, monitoring, remediation, and provisioning
• Evaluate, fine-tune, and integrate open-source and commercial LLMs into enterprise infrastructure tooling
• Build RAG pipelines, vector databases, and knowledge-grounding systems over infrastructure documentation, runbooks, and CMDB data
• Write production-grade Python code and Bash/PowerShell scripts
• Integrate AI solutions with Azure, AWS, GCP, ServiceNow, Datadog, Splunk, Prometheus/Grafana, and CI/CD pipelines
• Define and enforce AI governance guardrails for data privacy, model security, hallucination control, and cost/token management
• Partner with infrastructure leadership to identify high-ROI AI use cases and build the roadmap
• Mentor infrastructure engineers on AI-adjacent skills and act as internal AI Center of Excellence lead for the infrastructure vertical
• Own the POC-to-pilot-to-production lifecycle for AI initiatives, including MLOps/LLMOps practices

Requirements

• 10–12 years in Infrastructure/Cloud engineering or architecture, with the last 3–4 years focused on applied AI/ML/LLM work
• Strong hands-on coding ability in Python; scripting in Bash/PowerShell
• API integration and SDK usage, including OpenAI, Anthropic, LangChain, and LlamaIndex
• Understanding of LLM fundamentals: prompting, fine-tuning, embeddings, RAG, context windows, and tokens/cost
• Practical experience building agentic AI systems using LangGraph, AutoGen, CrewAI, or custom orchestration with tool-calling/function-calling
• Experience with vector databases such as Pinecone, Weaviate, FAISS, and Azure AI Search
• Deep infrastructure background in cloud architecture, networking, virtualization, ITSM, monitoring/observability, and automation using Ansible/Terraform
• Experience with MLOps/LLMOps, including model deployment, monitoring, versioning, and cost governance
• Strong architecture and solutioning skills
• Certifications in AWS/Azure/GCP AI or Solutions Architect are good-to-have
• Exposure to enterprise AI governance/responsible AI frameworks is good-to-have
• Experience presenting to CXO/leadership on AI strategy and business cases is good-to-have
• Prior experience in a Big 4/GDS/large enterprise infrastructure environment is good-to-have
• Open-source contributions or published POCs in AI/agentic systems are good-to-have
• Ability to translate ambiguous infrastructure pain points into AI-solvable use cases
• Strong stakeholder management
• Comfortable with hands-on coding/architecture and strategic roadmap/governance work

Core Competencies

Demonstrates expertise in architecting and delivering AI/ML/LLM-based solutions for infrastructure operations, with strong capabilities in Python coding, AI governance, and MLOps practices. Proven ability to mentor teams and translate infrastructure challenges into AI-driven solutions.

Highest-signal resume keywords

• AI/ML/LLM Solution Architecture
• Python Coding
• MLOps/LLMOps Practices
• API Integration
• Infrastructure Automation

ATS Optimization Keywords

Hard Skills

• Python
• Bash Scripting
• PowerShell Scripting
• API Integration
• LLM Fundamentals
• Agentic AI Systems
• Vector Databases
• Cloud Architecture
• MLOps
• Automation with Ansible/Terraform

Soft Skills

• Stakeholder Management
• Mentoring
• Strategic Roadmap Development

Certifications & Qualifications

• AWS AI Solutions Architect
• Azure AI Solutions Architect
• GCP AI Solutions Architect

Industry Keywords

• Infrastructure Operations
• ITOps
• AIOps
• Predictive Capacity
• Self-Healing Systems
• AI Governance
• Enterprise AI
• Big 4
• GDS
• ITSM

Tools & Technologies

• Azure
• AWS
• GCP
• ServiceNow
• Datadog
• Splunk
• Prometheus
• Grafana
• LangChain
• LlamaIndex