ML Ops & LLM Ops Engineer-Senior Associate-Analytics as service - Consult

Industry/Sector

Not Applicable

Specialism

Managed Services

Management Level

Senior Associate

Job Description & Summary

At PwC, our people in managed services focus on a variety of outsourced solutions and support clients across numerous functions. These individuals help organisations streamline their operations, reduce costs, and improve efficiency by managing key processes and functions on their behalf. They are skilled in project management, technology, and process optimization to deliver high-quality services to clients.

Those in managed service management and strategy at PwC will focus on transitioning and running services, along with managing delivery teams, programmes, commercials, performance and delivery risk. Your work will involve the process of continuous improvement and optimising of the managed services process, tools and services.

Focused on relationships, you are building meaningful client connections, and learning how to manage and inspire others. Navigating increasingly complex situations, you are growing your personal brand, deepening technical expertise and awareness of your strengths. You are expected to anticipate the needs of your teams and clients, and to deliver quality. Embracing increased ambiguity, you are comfortable when the path forward isn’t clear, you ask questions, and you use these moments as opportunities to grow.

Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:

• Respond effectively to the diverse perspectives, needs, and feelings of others.
• Use a broad range of tools, methodologies and techniques to generate new ideas and solve problems.
• Use critical thinking to break down complex concepts.
• Understand the broader objectives of your project or role and how your work fits into the overall strategy.
• Develop a deeper understanding of the business context and how it is changing.
• Use reflection to develop self awareness, enhance strengths and address development areas.
• Interpret data to inform insights and recommendations.
• Uphold and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance), the Firm's code of conduct, and independence requirements.

Senior Associate – MLOps / LLMOps Engineer

Role: Senior Associate – MLOps / LLMOps Engineer
Level: Senior Associate

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

Experience: 5–8 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: Anywhere in India (Preferably Hyderabad / Bangalore)

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.

Travel Requirements

Not Specified

Job Posting End Date