MLOps Engineer (Python, AWS &

- DevOps) Job Title

Senior MLOps Engineer

Experience

8 Years

Location

Hyderabad (or Pan India as applicable)

Job Summary

We are seeking an experienced MLOps Engineer with 8 years of IT experience and strong expertise in Python, Machine Learning, AWS Cloud, DevOps, and MLOps. The ideal candidate will be responsible for designing, deploying, automating, and maintaining scalable machine learning platforms and production ML pipelines. The role requires hands-on experience with model deployment, CI/CD, containerization, cloud infrastructure, and monitoring to support enterprise AI/ML solutions.

Mandatory Technical Skills 1.

Python

Development (Mandatory)

- Strong hands-on experience in Python programming.

- Experience with FastAPI, Flask, and REST API development.

- Knowledge of object-oriented programming, multithreading, and scripting.

- Experience developing backend services for ML applications.

- Machine Learning (Mandatory)

- Experience building, training, and deploying Machine Learning models.

- Strong understanding of supervised and unsupervised learning algorithms.

- Experience with feature engineering, model evaluation, and hyperparameter tuning.

- Hands-on experience with Scikit-learn, Pandas, NumPy, TensorFlow or PyTorch.

- MLOps (Mandatory)

- Design and implement end-to-end MLOps pipelines.

- Experience with model versioning, deployment, monitoring, and retraining.

- Hands-on experience with MLflow, Kubeflow, SageMaker, or similar MLOps platforms.

- Experience in model lifecycle management and experiment tracking.

- AWS Cloud (Mandatory)

Robust Experience With AWS Services Including

- Amazon SageMaker

- EC2

- S3

- ECR

- ECS/EKS

- Lambda

- IAM

- CloudWatch

- VPC

- API Gateway

- CloudFormation

- DevOps &
- Automation (Mandatory)

- CI/CD pipeline development using Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps.

- Infrastructure as Code using Terraform or CloudFormation.

- Git version control.

- Shell scripting and automation.





Key Responsibilities 1. MLOps Platform Development

- Design, build, and maintain scalable MLOps platforms.

- Automate ML model training, validation, deployment, and monitoring.

- Develop reusable ML deployment pipelines.

- Machine Learning Deployment

- Deploy ML models into production environments.

- Implement model serving using REST APIs.

- Monitor prediction accuracy and model drift.

- Support model retraining and lifecycle management.

- Backend Development

- Develop backend applications using Python.

- Build RESTful APIs using FastAPI or Flask.

- Integrate ML models with enterprise applications.

- Cloud Infrastructure

- Deploy ML workloads on AWS.

- Manage cloud infrastructure for ML environments.

- Optimize cloud resources for cost and performance.

- Containerization &
- Orchestration

- Containerize ML applications using Docker.

- Deploy workloads on Kubernetes (EKS).

- Manage scalable container-based deployments.

- CI/CD Automation

- Build automated pipelines for ML model deployment.

- Automate testing, deployment, and rollback processes.

- Integrate Infrastructure as Code into deployment workflows.

- Monitoring &

- Observability

- Monitor deployed ML models and infrastructure.

- Implement logging, alerting, and performance monitoring.

- Analyze model performance and operational metrics.

- Security &

- Governance

- Implement IAM policies and cloud security best practices.

- Secure ML pipelines and cloud resources.

- Ensure compliance with organizational standards.

- Collaboration

- Work closely with Data Scientists, Data Engineers, DevOps Engineers, and Application Developers.





- Participate in Agile ceremonies.

- Mentor junior engineers and support technical decision-making.

Required Technical Skills Programming

- Python

- FastAPI

- Flask

- REST APIs

Machine Learning

- Scikit-learn

- Pandas

- NumPy

- TensorFlow / PyTorch

- Model Evaluation

- Feature Engineering

MLOps

- MLflow

- Kubeflow

- SageMaker

- Model Registry

- Experiment Tracking

- Model Monitoring

Cloud

- AWS

- EC2

- S3

- IAM

- Lambda

- CloudWatch

- EKS

- ECS

- API Gateway

DevOps

- Docker

- Kubernetes

- Jenkins

- GitHub Actions

- GitLab CI/CD

- Terraform

- Git

Monitoring

- Prometheus

- Grafana

- CloudWatch

- ELK / Splunk

Databases

- PostgreSQL

- MySQL

- MongoDB

Operating Systems

- Linux

- Shell Scripting

Good-to-Have Skills

- Generative AI

- Large Language Models (LLMs)

- LangChain

- LangGraph

- Retrieval-Augmented Generation (RAG)

- Vector Databases (Pinecone, FAISS, ChromaDB)

- Apache Airflow

- Kafka

- Feature Store implementation

- GPU-based model deployment

- AI Security and Responsible AI

Responsibilities / Expectations from the Role

- Build and maintain scalable MLOps platforms for enterprise AI solutions.

- Develop backend Python services and deploy ML models to production.

- Automate ML lifecycle using CI/CD, Docker, Kubernetes, and AWS.

- Monitor model performance, detect drift, and optimize deployments.

- Collaborate with cross-functional teams to deliver secure, scalable, and reliable ML solutions.

Education

- Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, or a related field.

Preferred Certifications

- AWS Certified Machine Learning Specialty

- AWS Certified DevOps Engineer Professional

- AWS Certified Solutions Architect Associate

- Certified Kubernetes Administrator (CKA)

- TensorFlow Developer Certification

- Databricks Machine Learning Professional (Preferred)

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