Consultant - Machine Learning Engineer

Crescendo Global Leadership Hiring India; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Crescendo Global Leadership Hiring India is seeking an experienced Machine Learning Engineer to build, deploy, and operate scalable production ML systems in Bengaluru or Gurugram, with emphasis on MLOps, ML serving, and cloud infrastructure.

Responsibilities include designing end-to-end ML pipelines, real-time and batch inference, model versioning, automated retraining, and robust monitoring; you will work with Data Scientists to productionize research into reliable services.

Qualifications

• 6-9 years of software engineering experience with 4+ years of production ML experience.
• Strong hands-on expertise in Java, Scala, or Python with solid software practices and testing.

Responsibilities

• Design, build, and operate production ML pipelines including feature engineering, model training, serving, monitoring, and retraining.
• Build scalable, low-latency ML model serving infrastructure for real-time and batch inference.
• Own ML CI/CD processes including model versioning, automated retraining, deployments, canary releases, and rollbacks.
• Develop batch and streaming feature pipelines with Spark and Kafka.
• Implement observability, data-quality checks, drift detection, SLA monitoring, and alerting for ML systems.
• Collaborate with Data Scientists and ML Specialists to productionize research prototypes.
• Optimize ML infrastructure for performance and cost, including GPU utilization and batching.
• Contribute to architecture decisions for MLOps tooling and distributed systems.
• Participate in production support, incident response, and reliability improvements.
• Mentor engineers and lead architectural reviews for ML platform components.

Skills

Java

Python

Scala

Distributed systems

MLOps

CI/CD

Spark

Kafka

TorchServe/TF Serving

Terraform

Kubernetes

Docker

Education

Bachelor's degree in Computer Science

Tools

AWS

Kubernetes

Docker

Terraform

MLflow

SageMaker Pipelines

Airflow

Job description

Consultant - Machine Learning Engineer-6-9 years- Bangalore / Gurgaon
We are looking for an experienced Machine Learning Engineer to build, deploy, and operate scalable production ML systems. The role focuses on MLOps, ML serving, distributed systems, cloud infrastructure, and production-grade ML pipelines.

Location Bengaluru / Gurugram

Your Future Employer - A leading global AI and analytics organization working across advanced analytics, data engineering, MLOps, Generative AI, and AI-powered solutions. The organization partners with major businesses to solve complex problems through data-driven technology and innovation.

Responsibilities -

1. Design, build, and operate production ML pipelines covering feature engineering, model training, serving, monitoring, and retraining.
2. Build and maintain scalable, low-latency ML model serving infrastructure for real-time and batch inference.
3. Own ML CI/CD processes including model versioning, automated retraining, deployments, canary releases, and rollback strategies.
4. Build robust batch and streaming feature pipelines using technologies such as Spark and Kafka.
5. Implement observability, data-quality checks, drift detection, SLA monitoring, and alerting for ML systems.
6. Work closely with Data Scientists and ML Specialists to productionize research prototypes into scalable and maintainable services.
7. Optimize ML infrastructure for performance and cost, including GPU utilization, batching, caching, and model optimization.
8. Contribute to architecture and platform decisions for MLOps tooling, ML infrastructure, and distributed systems.
9. Participate in production support, incident response, postmortems, and reliability improvements.
10. Mentor engineers and lead technical design and architecture reviews for ML platform and serving components.

Requirements -

1. 6-9 years of software engineering experience with 4+ years of experience building and operating production ML systems.
2. Strong hands-on expertise in Java, Scala, or Python with good knowledge of software engineering practices, testing, design patterns, and code quality.
3. Strong experience with AWS cloud services including EC2, EKS/Kubernetes, S3, Lambda, and IAM.
4. Experience with Kafka, Spark, distributed systems, event-driven pipelines, and scalable microservices.
5. Hands-on experience with ML serving frameworks such as TorchServe, TensorFlow Serving, Triton, or custom/gRPC-based services.
6. Experience with MLOps and CI/CD tools such as MLflow, SageMaker Pipelines, Airflow, Jenkins, GitHub Actions, or similar platforms.
7. Strong understanding of data validation, unit/integration testing, model testing, and prevention of train/serve skew.
8. Experience with Terraform or similar infrastructure-as-code tools, Docker, and Kubernetes.
9. Knowledge of ML fundamentals and the ability to collaborate effectively with Data Scientists and ML teams.
10. Experience owning production systems, including SLAs, on-call support, incident management, and capacity planning.

What is in it for you -

1. Opportunity to work on large-scale production ML and MLOps systems.
2. Exposure to advanced AI, cloud, distributed systems, and Generative AI technologies.
3. Opportunity to work with experienced Data Scientists, ML Engineers, and technology professionals.
4. Scope to contribute to architecture, platform engineering, and high-impact ML initiatives.
5. Opportunities for technical leadership, mentoring, and continuous learning.

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