MLOP

Job Description 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)

Strong 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)