Ai Ml Engineer

Pi Square Technologies; Hyderabad, Pune District, Bengaluru; Sur place; INR 1 800 000 - 2 600 000; Plein temps

Résumé du poste

Pi Square Technologies is seeking a hands-on Forward Deployed Engineer (FDE) specializing in Generative AI, hyperscale cloud platforms, DevOps, and platform engineering.

You will design, deploy and operate AI agents across enterprise environments, ensuring reliability, security, and scalability while integrating with CI/CD pipelines, observability tools, and enterprise IT systems. The role calls for strong Python skills and production AI experience.

Qualifications

• 5+ years in software/cloud engineering or related roles.
• Hands-on with GenAI and enterprise AI deployments.
• Experience with LangChain, LangGraph, and AI frameworks.
• Experience deploying AI/GenAI in production.
• Knowledge of prompt engineering, RAG, vector databases, tool calling.

Responsabilités

• Design, deploy, and operationalize GenAI agents across client environments.
• Develop agent workflows for automation, incident response, and RCA.
• Deploy AI solutions on Azure, AWS, GCP and Kubernetes.
• Integrate agents with CI/CD, observability, ITSM, and code repos.
• Create reusable APIs, connectors, prompts, and automation frameworks.
• Implement AI governance: guardrails, access control, audit logging, cost controls.
• Own deployment, troubleshooting, observability, and continuous improvement.
• Collaborate with DevOps, SRE, Security, Platform, and Engineering teams.

Connaissances

Python

LangChain

LangGraph

Agentic AI

LLM Tech

Kubernetes

Docker

Terraform

CI/CD

GitHub Actions

Outils

AKS

EKS

GKE

Azure DevOps

Jenkins

GitHub Actions

Prometheus

Grafana

Description du poste

Role & responsibilities

Forward Deployed Engineer (AI, Hyperscale Cloud & DevOps)

Role Summary

We are seeking a hands-on Forward Deployed Engineer (FDE) with expertise in Generative AI, Hyperscaler Cloud Platforms, DevOps, and Platform Engineering. The role involves designing, deploying, integrating, and operationalizing AI agents and cloud-native solutions across enterprise environments while ensuring reliability, scalability, security, and operational excellence.

Key Responsibilities

• Design, develop, and deploy AI/GenAI agents using Agentic AI frameworks and LLM technologies.
• Build and operationalize agent workflows for automation, incident response, RCA, deployment validation, and reliability engineering.
• Deploy and manage AI solutions across hyperscaler cloud platforms (Azure, AWS, GCP) and Kubernetes environments.
• Integrate agents with CI/CD pipelines, observability tools, ITSM platforms, code repositories, and enterprise systems.
• Develop reusable APIs, connectors, prompt templates, and automation frameworks.
• Implement AI governance controls including guardrails, access controls, audit logging, monitoring, cost optimization, and rollback mechanisms.
• Own deployment, troubleshooting, observability, and continuous improvement of AI agent platforms.
• Collaborate with DevOps, SRE, Security, Platform, and Engineering teams to deliver production-ready solutions.

Required Skills & Experience

• 5+ years of experience in Software Engineering, Cloud Engineering, DevOps, SRE, AI/ML Engineering, or related roles.
• Strong programming skills in Python with experience building AI/GenAI applications.
• Hands‑on experience with LangChain, LangGraph, Agentic AI frameworks, and LLM technologies.
• Experience deploying AI/GenAI solutions in production environments.
• Strong understanding of:
  • Prompt Engineering
  • RAG
  • Vector Databases
  • Tool Calling
  • Agent Orchestration
  • Structured Outputs
• Strong experience with one or more Hyperscalers: Azure, AWS, or GCP.
• Hands‑on experience with:
  • Kubernetes (AKS/EKS/GKE)
  • Docker
  • Terraform
  • GitHub Actions / Azure DevOps / Jenkins
  • CI/CD and Infrastructure as Code (IaC)
• Experience with observability and monitoring tools such as Grafana, Prometheus, OpenTelemetry, Splunk, Dynatrace, or Datadog.
• Understanding of DevOps, SRE, cloud reliability, security, and operational best practices.

Preferred Skills

• Multi‑cloud architecture and deployment experience across Azure, AWS, and GCP.
• Experience with AI Reliability Engineering, LLMOps, MCP, Agent Gateways, and Responsible AI practices.
• Exposure to .NET or Java application development.
• Experience working in enterprise or regulated environments.
• Certifications in Azure, AWS, GCP, Kubernetes, AI Engineering, or DevOps.

Behavioral Attributes

• Strong ownership mindset with a "get things done" attitude.
• Excellent problem‑solving and multitasking abilities.
• Ability to work directly with client and platform teams.

Curious, adaptable, and eager to learn emerging AI and hyperscaler technologies