MLops Engineer

PwC; Hyderabad, Bengaluru; Hybrid; INR 1,800,000 - 2,800,000; Full time

Job summary

PwC is seeking a Senior Associate MLOps/LLMOps Engineer to design, build, and operate cloud-native AI and ML delivery pipelines in an enterprise AWS environment. You will work with AI engineers, data scientists, and operators to ensure models and services are versioned, monitored, and deployed with confidence across development to production.

This hands-on role emphasizes automation, reliability, and controlled production releases, partnering with cross-functional teams in Bangalore and

Qualifications

• Bachelor’s degree in CS/engineering or related field with cloud/DevOps focus.
• 5–10 years of hands-on experience in MLOps/DevOps in enterprise cloud projects.
• Experience in AWS-based AI/ML delivery pipelines and governance.

Responsibilities

• Design and operate cloud-native AI/ML delivery pipelines in enterprise AWS.
• Implement MLOps/LLMOps patterns for training, packaging, deployment, lifecycle.
• Design GitHub-based CI/CD pipelines for models and services.
• Manage versioning of models, prompts, configurations, and artifacts.
• Secure secrets, enforce least-privilege access, and support production releases.

Skills

AWS Cloud

MLOps

LLMOps

CI/CD

DevOps

Versioning

Security

Observability

Collaboration

Education

Bachelor's in Computer Science or related field

Tools

GitHub

IaC

Job description

Role: Senior Associate MLOps / LLMOps Engineer

Level: Senior Associate

Tower: AI Platform Engineering & MLOps (AI Managed Services)

Experience: 5–10 years

Key Skills: AWS Cloud & Infrastructure; MLOps & LLMOps; DevOps & CI/CD; Model & Artifact Versioning; Secure Deployments; Observability & Release Governance

Educational Qualification:

Bachelor’s degree in Computer Science, Engineering, or related field (Master’s or relevant cloud/DevOps certifications preferred)

Work Location: Bangalore and Hyderabad (based on your preference)

Job Description

As a Senior Associate – MLOps / LLMOps Engineer, you will design, build, and operate cloud-native AI and ML delivery pipelines that enable reliable, secure, and governed promotion of models and AI services from development to production. You will partner with AI engineers, data scientists, and operations teams to ensure models, prompts, and AI services are versioned, monitored, and deployed with confidence in an enterprise AWS environment.

This role is hands‑on and execution‑focused, emphasizing automation, reliability, and controlled production releases for ML and LLM-based systems.

Key Responsibilities

AWS Cloud & Infrastructure Engineering

• Build and maintain AWS‑based infrastructure supporting ML, LLM, and AI platforms.
• Use infrastructure‑as‑code principles to ensure repeatable and auditable environments.
• Configure IAM roles, networking, logging, and monitoring aligned to enterprise standards.

MLOps & LLMOps Enablement

• Implement MLOps and LLMOps patterns to support model training, packaging, deployment, and lifecycle management.
• Support deployment of traditional ML models as well as LLM‑based services and workflows.
• Enable reproducibility across environments through standardized pipelines and artifacts.

CI/CD & DevOps Automation

• Design and maintain GitHub‑based CI/CD pipelines for ML models, AI services, and infrastructure changes.
• Automate build, test, packaging, and deployment workflows.
• Enforce quality gates and approvals prior to environment promotion.

Versioning & Release Management

• Manage versioning of models, prompts, configurations, and artifacts across environments.
• Support controlled promotion from development to test, staging, and production.
• Implement rollback strategies and release validation checks to minimize production risk.

Secrets & Configuration Management

• Securely manage secrets, credentials, and sensitive configuration using AWS‑native and approved enterprise tooling.
• Enforce least‑privilege access and rotation policies.
• Ensure separation of configuration across environments.

Deployment & Environment Management

• Deploy AI and ML services using containerized and cloud-native patterns.
• Support blue/green, canary, or phased deployments where applicable.
• Ensure deployments are repeatable, traceable, and compliant with change governance.

Monitoring, Logging & Observability

• Implement monitoring and alerting for AI services, model endpoints, and pipelines.
• Track service health, deployment status, and runtime performance.
• Support operational dashboards and metrics for platform and service visibility.

Production Support & Controlled Promotion

• Partner with operations teams to support production readiness and stability.
• Participate in release readiness reviews and production cutovers.
• Ensure promotion to production follows defined governance, approvals, and validation criteria.

Collaboration & Continuous Improvement

• Collaborate with AI engineers, data scientists, and platform teams to streamline delivery workflows.
• Identify opportunities to improve reliability, security, and developer productivity.
• Contribute reusable pipeline templates, standards, and documentation.

Required Skills

• Hands‑on experience with AWS cloud services and infrastructure.
• Strong understanding of MLOps and LLMOps concepts and lifecycle management.
• Experience building CI/CD pipelines using GitHub.
• Solid DevOps fundamentals, including automation and environment management.
• Experience managing secrets and secure configurations.
• Familiarity with model and artifact versioning practices.
• Experience deploying services and supporting controlled production releases.
• Strong collaboration and documentation skills.

Preferred Skills

• Experience with containerized deployments and orchestration platforms.
• Familiarity with enterprise monitoring and logging tools.
• Exposure to governance, risk, and compliance requirements for AI systems.
• AWS certifications (Developer, DevOps Engineer, Solutions Architect).
• Experience supporting regulated or large‑scale enterprise environments.