Lead Platform Engineer

EPAM Systems; India; On-site; INR 3,000,000 - 5,500,000; Full time

Job summary

EPAM Systems in India is seeking an experienced Lead Platform Engineer to join our Automation Engineering team. You will architect and scale cloud infrastructure automation, GenAI workflows, and ML-driven AIOps, driving operational efficiency and robust DevOps practices.

Responsibilities span IaC with Terraform, CI/CD, Python scripting, and integrating AI models across platforms like Bedrock, Vertex AI, and OpenSearch-based vector search.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 7+ years of experience in cloud infrastructure automation, scripting, and DevOps.
• Strong proficiency in IaC tools like Terraform, CloudFormation, or similar.
• Expertise in Python, cloud AI frameworks such as LangChain, and generative AI workflows.
• Demonstrated background in developing and deploying AI models such as RAG or transformers.
• Proficiency in building vector databases and document sources using solutions like OpenSearch or Amazon Kendra.
• Competency in preparing and labeling datasets for AI models and optimizing data inputs.
• Familiarity with cloud platforms including AWS, Google Cloud, or Azure.
• Capability to implement MLOps pipelines and monitor AI system performance.

Responsibilities

• Design and develop automated workflows for cloud infrastructure provisioning using IaC tools like Terraform.
• Build frameworks to support deployment, configuration, and management across diverse cloud environments.
• Develop and manage service catalog components, ensuring integration with platforms like Backstage.
• Implement GenAI models to enhance service catalog functionality and code quality across automation pipelines.
• Design and implement CI/CD pipelines and maintain CI pipeline code for cloud automation use cases.
• Write scripts to support cloud deployment orchestration using Python, Bash, or other scripting languages.
• Design and deploy generative AI models for AIOps applications such as anomaly detection and predictive maintenance.
• Work with frameworks like LangChain or cloud platforms such as Bedrock, Vertex AI, and Azure AI to deploy RAG workflows.
• Build and optimize vector databases and document sources using tools like OpenSearch, Amazon Kendra, or equivalent solutions.
• Prepare and label data for generative AI models, ensuring scalability and integrity.
• Create agentic workflows using frameworks like Langraph or cloud GenAI platforms such as Bedrock Agents.
• Integrate generative AI models with operational systems and AIOps platforms for enhanced automation.
• Evaluate AI model performance and ensure continuous optimization over time.
• Develop and maintain MLOps pipelines to monitor and mitigate model decay.
• Collaborate with cross‑functional teams to drive innovation and improve cloud automation processes.
• Research and recommend new tools and best practices to enhance operational efficiency.

Skills

Cloud infrastructure automation

DevOps

Scripting (Python, Bash)

IaC (Terraform, CloudFormation)

GenAI / AI models (RAG, transformers)

MLOps

Data labeling / data prep

OpenSearch / vector databases

Cross-functional collaboration

Education

Bachelor's or Master's degree in Computer Science, Engineering, or a related field

Tools

Terraform

CloudFormation

LangChain

Bedrock

Vertex AI

Azure AI

OpenSearch

Amazon Kendra

Backstage

Langraph

Python

Bash

Job description

We are seeking an experienced Lead Platform Engineer to join our Automation Engineering team. The ideal candidate will excel in cloud infrastructure automation, generative AI, and machine learning, with a strong foundation in DevOps practices and modern scripting tools.

The role involves designing cutting‑edge AI‑driven solutions for AIOps while innovating cloud automation processes to optimize operational efficiency.

Responsibilities

• Design and develop automated workflows for cloud infrastructure provisioning using IaC tools like Terraform.
• Build frameworks to support deployment, configuration, and management across diverse cloud environments.
• Develop and manage service catalog components, ensuring integration with platforms like Backstage.
• Implement GenAI models to enhance service catalog functionality and code quality across automation pipelines.
• Design and implement CI/CD pipelines and maintain CI pipeline code for cloud automation use cases.
• Write scripts to support cloud deployment orchestration using Python, Bash, or other scripting languages.
• Design and deploy generative AI models for AIOps applications such as anomaly detection and predictive maintenance.
• Work with frameworks like LangChain or cloud platforms such as Bedrock, Vertex AI, and Azure AI to deploy RAG workflows.
• Build and optimize vector databases and document sources using tools like OpenSearch, Amazon Kendra, or equivalent solutions.
• Prepare and label data for generative AI models, ensuring scalability and integrity.
• Create agentic workflows using frameworks like Langraph or cloud GenAI platforms such as Bedrock Agents.
• Integrate generative AI models with operational systems and AIOps platforms for enhanced automation.
• Evaluate AI model performance and ensure continuous optimization over time.
• Develop and maintain MLOps pipelines to monitor and mitigate model decay.
• Collaborate with cross‑functional teams to drive innovation and improve cloud automation processes.
• Research and recommend new tools and best practices to enhance operational efficiency.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 7+ years of experience in cloud infrastructure automation, scripting, and DevOps.
• Strong proficiency in IaC tools like Terraform, CloudFormation, or similar.
• Expertise in Python, cloud AI frameworks such as LangChain, and generative AI workflows.
• Demonstrated background in developing and deploying AI models such as RAG or transformers.
• Proficiency in building vector databases and document sources using solutions like OpenSearch or Amazon Kendra.
• Competency in preparing and labeling datasets for AI models and optimizing data inputs.
• Familiarity with cloud platforms including AWS, Google Cloud, or Azure.
• Capability to implement MLOps pipelines and monitor AI system performance.

Nice to Have

• Knowledge of agentic architectures such as React and flow engineering techniques.
• Background in using Bedrock Agents or Langraph for workflow creation.
• Understanding of integrating generative AI into legacy or complex operational systems.