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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