Sr Technical Lead-Cloud & Infra Engg

Birlasoft; India; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Birlasoft in Noida is seeking a Sr Technical Lead-Cloud & Infra Engg to architect and deliver scalable MLOps platforms. You will lead end-to-end pipelines, GenAI deployment, and cloud-native infrastructure across Azure and AWS, with Kubernetes orchestration and IaC practices.

Collaborate with data scientists, AI engineers, and security teams to implement governance, cost optimization, and robust CI/CD for ML workloads, driving production readiness and continuous improvement.

Qualifications

• End-to-end MLOps involves data ingestion, feature engineering, model training, validation and deployment.
• Experience operationalizing GenAI and LLM-based solutions with prompts, embeddings and tools.
• CI/CD for ML/GenAI workloads using best practices and governance.
• Model lifecycle management, versioning, rollback and monitoring.

Responsibilities

• Design and implement end-to-end MLOps pipelines covering data ingestion, feature engineering, model training, validation, deployment, monitoring, and retraining.
• Operationalize GenAI and LLM-based solutions including prompt management, vector databases, embeddings, and agent workflows.
• Implement CI/CD pipelines for ML and GenAI workloads using industry best practices.
• Enable scalable experimentation, versioning, rollback, and model lifecycle management.
• Build and operate Agentic AI frameworks supporting multi-agent orchestration, tool calling, memory management, and autonomous task execution.
• Implement guardrails for GenAI including security, safety, bias detection, hallucination mitigation, and policy enforcement.
• Optimize LLM inference performance, latency, cost, and throughput across environments.
• Architect and manage cloud-native ML platforms on Azure and/or AWS.
• Leverage cloud services for compute, storage, containerization, and orchestration (Kubernetes).
• Implement infrastructure-as-code (IaC) and platform automation to support scalable MLOps operations.
• Implement monitoring for model performance, data drift, concept drift, and system health.
• Ensure compliance with data governance, security, auditability, and Responsible AI.
• Collaborate with FinOps teams to manage GenAI and ML platform costs.
• Work closely with Data Scientists, AI Engineers, Cloud Architects, SRE, and Security teams.
• Support production readiness reviews, incident resolution, and continuous improvement initiatives.
• Contribute to reusable accelerators, reference architectures, and best practices.

Skills

MLOps

GenAI

CI/CD

Observability

Security

Governance

Tools

Azure

AWS

Kubernetes

IaC (Terraform/ARM)

Job description

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Location: INDIA - NOIDA- BIRLASOFT OFFICE

Title: Sr Technical Lead-Cloud & Infra Engg

Description:

Area(s) of responsibility

Data & MLOps Engineering

• Design and implement end-to-end MLOps pipelines covering data ingestion, feature engineering, model training, validation, deployment, monitoring, and retraining.
• Operationalize GenAI and LLM-based solutions, including prompt management, vector databases, embeddings, and agent workflows.
• Implement CI/CD pipelines for ML and GenAI workloads using industry best practices.
• Enable scalable experimentation, versioning, rollback, and model lifecycle management.

GenAI & Agentic AI Enablement

• Build and operate Agentic AI frameworks supporting multi-agent orchestration, tool calling, memory management, and autonomous task execution.
• Implement guardrails for GenAI including security, safety, bias detection, hallucination mitigation, and policy enforcement.
• Optimize LLM inference performance, latency, cost, and throughput across environments.

Cloud Infrastructure & Platform Engineering

• Architect and manage cloud-native ML platforms on Azure and/or AWS.
• Leverage cloud services for compute (CPU/GPU), storage, containerization, and orchestration (Kubernetes).
• Implement infrastructure-as-code (IaC) and platform automation to support scalable MLOps operations.

Observability, Governance & FinOps

• Implement monitoring for model performance, data drift, concept drift, and system health.
• Ensure compliance with enterprise standards for data governance, security, auditability, and Responsible AI.
• Collaborate with FinOps teams to manage and optimize GenAI and ML platform costs.

Collaboration & Delivery

• Work closely with Data Scientists, AI Engineers, Cloud Architects, SRE, and Security teams.
• Support production readiness reviews, incident resolution, and continuous improvement initiatives.
• Contribute to reusable accelerators, reference architectures, and best practices.