The Top MLOps Jobs in 2025

MLOps jobs are experiencing explosive growth, with the market expected to surge from USD 1.1 billion in 2022 to USD 5.9 billion by 2027, at a remarkable CAGR of 41.0%. Indeed, this field has become one of the fastest-growing areas in tech, demonstrating an impressive 9.8× growth over just five years.

As organizations increasingly deploy machine learning models into production, MLOps roles have become a necessity rather than a luxury. If you’re considering a career in this field, the financial rewards are substantial.

This guide breaks down the top 8 MLOps jobs you should consider in 2025, along with their responsibilities, essential skills, and expected compensation in India.

Top 8 MLOps Jobs in 2025

The demand for professionals who can bridge the gap between AI development and operations continues to accelerate rapidly. As organizations invest in machine learning infrastructure, several specialized roles have emerged as particularly valuable. Let’s explore the most sought-after MLOps positions that will dominate the job market in 2025.

1. MLOps Engineer 

At the core of any ML-powered organization sits the MLOps Engineer, the crucial link between experimental data science and real-world applications. This role focuses on streamlining the deployment and management of machine learning models in production environments.

Key Responsibilities:

• Managing and supervising model development, deployment, tracking, and monitoring
• Automating ML workflows from training to validation to deployment
• Setting up continuous integration/continuous deployment (CI/CD) pipelines for ML
• Ensuring models remain functional in production despite data drift
• Collaborating with data scientists to optimize their models for real-world use

Core Skills Required:

• Proficiency in Python and ML frameworks like TensorFlow, PyTorch, scikit-learn
• Knowledge of containerization tools (Docker) and orchestration (Kubernetes)
• Familiarity with cloud platforms (AWS, Azure, GCP)
• Experience with monitoring tools (Prometheus, Grafana)

Average Salary in India: ₹6–10 LPA (entry), ₹10–20 LPA (mid), ₹20–35 LPA + (senior, varies by city)

2. ML Platform Architect 

The ML Platform Architect serves as the visionary behind an organization’s machine learning infrastructure. As companies scale their AI initiatives, this strategic role becomes increasingly vital for creating standardized, efficient systems that multiple teams can leverage.

Key Responsibilities:

• Designing and building scalable, reliable ML infrastructure
• Developing and maintaining internal ML tools and platforms
• Standardizing ML workflows across the organization
• Ensuring platform security and compliance
• Optimizing platform performance and cost-efficiency

Core Skills Required:

• Expertise in Python, Go, or Java for system-level programming
• Expert-level cloud computing and Kubernetes orchestration
• Skills in Infrastructure as Code (IaC), networking, and security
• Strategic thinking for long-term architectural decisions

Average Salary in India: ₹20–35 LPA + for experienced professionals; can exceed ₹35 LPA in top tech hubs

3. DevOps Engineer (ML Focus) 

DevOps Engineers with ML specialization apply established DevOps practices to the unique challenges of machine learning workflows. They create the foundation that enables efficient model deployment and management.

Key Responsibilities:

• Blending DevOps practices with ML workflows to build robust pipelines
• Implementing continuous integration/continuous delivery for ML models
• Creating automated testing frameworks for ML applications
• Managing infrastructure and cloud resources for ML workloads
• Collaborating with data scientists and ML engineers to streamline operations

Core Skills Required:

• Proficiency in CI/CD tools (Jenkins, GitHub Actions)
• Strong command of Docker and Kubernetes
• Knowledge of Terraform or CloudFormation
• Cloud platform experience and Python/shell scripting

Average Salary in India: ₹6–8 LPA (entry), ₹10–18 LPA (mid), ₹20–30 LPA + (senior roles)

4. Cloud Infrastructure Engineer 

As machine learning workloads increasingly move to the cloud, Cloud Infrastructure Engineers play a crucial role in designing and optimizing the environments where ML models operate and hence it is one of the top MLOps jobs out there. They ensure that computational resources are efficiently allocated for both training and inference.

Key Responsibilities:

• Optimizing cloud infrastructure for managing ML workloads
• Designing scalable environments for model training and deployment
• Implementing security best practices for ML systems
• Managing costs associated with compute-intensive ML operations
• Creating automation for resource provisioning and scaling

Core Skills Required:

• Deep knowledge of AWS, Azure, or GCP and their ML services
• Proficiency in Infrastructure as Code and networking concepts
• Strong understanding of security and distributed computing
• Experience with GPU-based ML workload optimization

Average Salary in India: ₹8–12 LPA (entry), ₹15–25 LPA (mid), ₹30 LPA + (senior)

5. AI/ML Research Scientist 

AI/ML Research Scientists bridge the gap between cutting-edge research and practical applications. They work on advancing ML algorithms while keeping deployment considerations in mind—a crucial perspective for successful MLOps implementations.

Key Responsibilities:

• Researching and developing novel ML algorithms and techniques
• Collaborating with engineering teams on model optimization
• Ensuring research models are deployment-ready
• Publishing findings and contributing to the scientific community
• Evaluating new ML approaches for potential business applications

Core Skills Required:

• Advanced education (Master’s/PhD) in ML, CS, or statistics
• Strong mathematical foundations and ML framework expertise
• Excellent communication for cross-team collaboration
• Deep understanding of algorithmic design and experimentation

Average Salary in India: ₹15–25 LPA (early-career), ₹30–50 LPA + (experienced researchers)

6. Data Pipeline Engineer (for ML Systems) 

Data Pipeline Engineers focused on ML systems play a critical role in ensuring that machine learning models have reliable, scalable access to high-quality data. They create the infrastructure that powers the entire ML lifecycle.

Key Responsibilities:

• Designing and implementing scalable data systems for ML models
• Building ETL/ELT pipelines to feed ML models effectively
• Implementing data versioning and lineage tracking
• Optimizing data flows for training and inference
• Ensuring data quality and consistency across ML environments

Core Skills Required:

• Expertise in Apache Spark, Kafka, Python, and SQL
• Experience with data versioning tools like DVC
• Understanding of data governance and feature stores
• Strong grasp of data engineering tools and principles

Average Salary in India: ₹6–10 LPA (entry), ₹12–18 LPA (mid), ₹20–30 LPA (senior)

7. Machine Learning Reliability Engineer (MLRE) 

Machine Learning Reliability Engineers focus on the operational aspects of ML systems, ensuring they perform consistently and reliably in production environments. This emerging specialist role combines site reliability engineering practices with machine learning expertise.

Key Responsibilities:

• Ensuring the reliability, availability, and performance of ML systems
• Implementing monitoring and observability solutions for models
• Troubleshooting and resolving production issues
• Creating automated remediation for common ML system failures
• Establishing and maintaining SLAs for ML applications

Core Skills Required:

• Proficiency in Prometheus, Grafana, and the ELK stack
• Expertise in Kubernetes and containerization
• Understanding ML-specific failure modes
• Strong Python skills and cloud platform experience

Average Salary in India: ₹8–12 LPA (entry), ₹15–25 LPA (mid), ₹25–35 LPA + (senior)

8. AI Product Engineer (with MLOps Background) 

AI Product Engineers with MLOps expertise bridge the gap between customer needs and technical implementation. They ensure that machine learning models deliver real business value while maintaining production quality.

Key Responsibilities:

• Translating business requirements into technical ML specifications
• Developing end-to-end AI applications with production readiness in mind
• Ensuring proper integration of ML components with other systems
• Optimizing model performance for specific product contexts
• Collaborating with product managers to define ML features

Core Skills Required:

• Strong software engineering and ML programming skills
• Understanding of MLOps principles and deployment workflows
• Familiarity with UX design and product methodologies
• Excellent communication and stakeholder management

Average Salary in India: ₹8–12 LPA (entry), ₹15–25 LPA (mid), ₹25–40 LPA + (senior roles)

The MLOps job landscape continues to evolve rapidly as organizations mature in their AI capabilities. Beyond these eight core roles, we’re seeing the emergence of specialized positions like LLMOps Engineers (focusing on large language models), AI Ethics Officers (ensuring responsible AI deployment), and ML Security Specialists (protecting models from adversarial attacks).

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Concluding Thoughts…

The MLOps field stands out as one of the most promising career paths in technology today. With explosive growth projections and salaries ranging from ₹6-50 LPA across various roles in India, MLOps positions offer exceptional financial rewards for professionals with the right skills.

Each role we’ve examined requires a unique blend of technical expertise. 

Nevertheless, certain foundational elements remain consistent—Python programming, cloud platform knowledge, containerization skills, and an understanding of ML frameworks. These core competencies serve as your entry point into this rapidly evolving field.

Your path forward might start with focusing on a core set of MLOps tools while developing expertise in a major cloud platform. This foundation will prepare you for any of the career paths we’ve explored. Additionally, staying current with emerging specializations like LLMOps or ML Security will position you at the cutting edge of this dynamic field.

FAQs

Q1. What are the top MLOps jobs expected to be in high demand in 2025? 

The top MLOps jobs in 2025 include MLOps Engineer, ML Platform Architect, DevOps Engineer with ML focus, Cloud Infrastructure Engineer, AI/ML Research Scientist, Data Pipeline Engineer for ML Systems, Machine Learning Reliability Engineer, and AI Product Engineer with MLOps background.

Q2. What is the average salary range for MLOps professionals in India? 

MLOps professionals in India can expect salaries ranging from ₹6-50 LPA, depending on the specific role and level of experience. Entry-level positions typically start around ₹6-12 LPA, while senior roles can command ₹30-50 LPA or more.

Q3. What are the core skills required for a career in MLOps? 

Key skills for MLOps careers include proficiency in Python programming, expertise in ML frameworks (like TensorFlow and PyTorch), knowledge of cloud platforms (AWS, Azure, GCP), containerization (Docker), and orchestration (Kubernetes). Additionally, understanding of CI/CD pipelines and data engineering concepts is valuable.

Q4. How is the MLOps job market expected to grow in the coming years? 

The MLOps market is projected to experience significant growth, with estimates suggesting an increase from USD 1.1 billion in 2022 to USD 5.9 billion by 2027, at a CAGR of 41.0%. This rapid expansion indicates strong job prospects in the field.

Q5. What makes MLOps roles particularly valuable in the tech industry? 

MLOps roles are highly valued due to their interdisciplinary nature, combining deep technical expertise with practical implementation skills. These positions bridge the gap between AI development and operations, making them crucial for organizations deploying machine learning models in production environments.