Senior Platform Engineer - Pune, India

BP is undergoing a significant transformation to become a leading renewable energy provider and achieve net-zero carbon emissions by 2050. To support this ambitious transition, we are seeking experienced Senior AI Platform Engineers to architect and maintain the infrastructure that drives our AI and ML initiatives across the organization.

This role is crucial for leading the design, development, and optimization of scalable and reliable AI platforms. You will deploy and operate cloud infrastructure and automation pipelines for AI/ML and generative AI workloads across Azure, AWS, and Databricks. Your responsibilities will include building secure, scalable environments (VMs, containers, Kubernetes clusters, Databricks workspaces) for deploying cloud AI services such as Azure OpenAI/Azure ML and AWS SageMaker/Bedrock.

A key focus will be on implementing continuous integration and deployment (CI/CD) and infrastructure-as-code (ARM/Bicep, AWS CDK, Terraform) to automate model release cycles. You will embed security, compliance, monitoring, and observability (logging, Prometheus/Grafana) to ensure service reliability and facilitate quick issue resolution. Collaboration with data scientists and application teams to integrate generative AI features, such as chatbots and co-pilot assistants, into enterprise applications is also vital.

Key Responsibilities:

• Lead the design and implementation of scalable AI/ML infrastructure on Azure and AWS.
• Build and manage cloud-native infrastructure (Azure, AWS, Databricks) for AI workloads using Infrastructure-as-Code (IaC) tools like Terraform and Bicep.
• Create reusable self-service tooling, templates, and CI/CD workflows for data scientists and ML engineers.
• Govern AI systems with access control, audit trails, policy enforcement, and compliance monitoring (e.g., GDPR).
• Implement GenAI workloads using Azure AI Foundry, Azure AI Hub, Azure OpenAI, Amazon Bedrock, Anthropic Claude, Hugging Face, LangChain, etc.
• Implement infrastructure and DevOps practices for Agentic AI solutions using native Azure and AWS AI services.
• Collaborate with security and architecture teams to embed cloud security best practices in the AI platform.
• Contribute to incident response, troubleshooting, and root cause analysis of ML and GenAI workload failures and latency issues.
• Implement MLOps practices to manage and optimize the lifecycle of machine learning models, including monitoring, versioning, and retraining.
• Collaborate with data scientists, software engineers, and other stakeholders to ensure effective integration of AI solutions within the business.
• Stay up to date with the latest advancements in AI, cloud computing, and DevOps practices, and integrate relevant technologies into the platform.
• Review cloud cost reports, identify opportunities for cost savings, and lead relevant initiatives.
• Mentor junior engineers, providing technical leadership and fostering a culture of continuous learning.
• Ensure compliance with industry standards and best practices for data security and privacy.

Requirements:

• Bachelor’s or master’s degree or equivalent experience in computer science, engineering, information systems, or a numerate degree.
• 5+ years of experience in platform engineering, with a proven track record of designing, deploying, and managing scalable and secure cloud-based infrastructures, leveraging both Azure and AWS services.
• Experience with Azure services such as Azure AI services, Azure Search, Azure ML, Databricks, Azure Kubernetes Service, and AWS services like AWS SageMaker, AWS Bedrock, and AWS Lambda.
• Exposure to Generative AI and Agentic AI ecosystems such as Azure OpenAI, Azure AI Foundry, Azure AI Hub, Bedrock, Anthropic Claude, OpenAI API, LlamaCloud, LangChain.
• Understanding of token usage, LLM prompt injection risks, jailbreak attempts, and mitigation techniques.
• Strong knowledge of governance, audit, observability, and compliance in cloud-based GenAI and ML ecosystems.
• Understanding of Azure AI Evaluation SDK and AI Red Teaming Prompt Security Scans.
• Experience with code assistant tools like Github Copilot, Cursor, and Claude Code is a plus.
• Expertise in Azure DevOps or AWS CodePipeline, including setting up and managing CI/CD pipelines.
• Advanced experience with Azure Blob Storage, Cosmos DB, SQL, Key Vault, AWS S3, DynamoDB, and AWS RDS, and their integrations with AI services.
• Advanced understanding of networking concepts, including DNS management, load balancing, VPNs, and virtual networks (VNets).
• Advanced understanding of security concepts, including IAM roles, identities, Azure policies, AWS SCPs.
• Experience in Advanced Authentication and Authorization Concepts across various cloud providers and platforms.
• Experience with Azure Policy, AWS SCP, AWS IAM, audit logging, Azure RBAC.
• Mastery of infrastructure-as-code tools such as Azure ARM/Bicep, Terraform, CloudFormation, or equivalent.
• Proficiency in networking, DNS, load balancers, and cloud engineering services.
• Knowledge in Python programming and AI/ML libraries (TensorFlow, PyTorch, Sci-Kit learn, etc.).
• Experience with containerization and orchestration tools such as Docker and Kubernetes.
• Knowledge about Azure Bot framework, APIM, Application Gateway, and M365 offerings like M365 Copilot is a plus. AWS CDK, AWS Python (Boto3) SDK knowledge is also beneficial.
• Experience with monitoring tools like Grafana, Prometheus, Application Insights, Log Analytics Workspaces, and Azure Monitor.
• Strong problem-solving and analytical skills.
• Strong communication and collaboration skills to work effectively with diverse teams.
• Proven leadership abilities to guide and mentor junior engineers.

Travel Requirement: No travel is expected with this role.

Relocation Assistance: This role is eligible for relocation within the country.

Remote Type: This position is a hybrid of office/remote working.