AWS AI/ML Engineer

Tata Consultancy Services; Hyderabad, Pune District, Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Tata Consultancy Services in Hyderabad, India, seeks an AWS AI/ML Engineer to design, build, deploy and scale AI/ML solutions on AWS, focusing on production-grade inference and scalable data pipelines.

You will use SageMaker, Lambda, ECS/EKS, and collaborate with data engineers and stakeholders to translate business problems into AI/ML solutions. 4–8+ years of AI/ML and data engineering experience preferred.

Qualifications

• Masters/BS in CS, Data Science, AI or related field.
• 4–8+ years of AI/ML or data engineering experience.
• Hands-on with AWS ML solutions on AWS.

Responsibilities

• Design and implement ML/classification, regression, forecasting, NLP or CV use cases.
• Build end-to-end ML pipelines on AWS from ingestion to deployment.
• Develop real-time and batch inference services.
• Apply feature engineering and model tuning.
• Use SageMaker for training, tuning, deployment and monitoring.
• Design scalable AWS architectures (Lambda, ECS/EKS, Step Functions).
• Ensure security, cost optimization, and observability for ML workloads.

Skills

Python

NLP

Computer Vision

Time-series models

Model evaluation

SageMaker

ML pipelines

APIs / inference services

SQL

Data engineering basics

Education

Bachelor's/Master's in CS/DS/AI

Tools

FastAPI

Flask

SageMaker Studio/Pipelines/Endpoints/Model Monitor

AWS services (S3, DynamoDB, RDS/Aurora, Glue, Redshift)

REST APIs

Job description

Role & responsibilities

The AWS AI/ML Engineer is responsible for designing, building, deploying, and scaling AI and Machine Learning solutions on AWS. The role focuses on developing ML models, data pipelines, and production-grade inference systems using AWS-managed services, while ensuring scalability, security, and operational excellence.
This role supports predictive analytics, computer vision, NLP, and emerging GenAI-enabled ML use cases across enterprise environments.

Key Responsibilities

AI/ML Solution Development

• Design and develop machine learning models for classification, regression, forecasting, NLP, or computer vision use cases
• Build end-to-end ML pipelines (data ingestion, training, validation, deployment) on AWS
• Develop and deploy real-time and batch inference services
• Apply feature engineering, model tuning, and evaluation techniques
• Use Amazon SageMaker for training, tuning, deployment, and monitoring of ML models
• Design scalable architectures using AWS Lambda, ECS/EKS, Step Functions
• Manage data storage and access using S3, DynamoDB, RDS/Aurora
• Ensure availability, performance, and cost optimization of ML workloads
• Implement CI/CD pipelines for ML models and data workflows
• Enable model versioning, monitoring, retraining, and rollback
• Track model performance, drift, and data quality
• Follow best practices for MLOps, automation, and observability
• Adhere to enterprise security, privacy, and compliance standards
• Work closely with data engineers, cloud architects, and business stakeholders
• Translate business problems into AI/ML solutions
• Support POCs, pilots, and production rollouts
• Contribute to reusable ML frameworks and accelerators

Required Skills & Skill Set

Machine Learning & AI

• Strong understanding of supervised and unsupervised ML algorithms
• Experience with NLP, Computer Vision, or time-series models
• Feature engineering, hyperparameter tuning, model evaluation
• Amazon SageMaker (Studio, Pipelines, Endpoints, Model Monitor)
• Experience with data pipelines on AWS
• Strong proficiency in Python
• Hands-on with scikit-learn, TensorFlow, PyTorch
• REST APIs, inference services using FastAPI / Flask
• SQL and basic data engineering skills
• Data ingestion and transformation pipelines
• S3, Athena, Glue, Redshift (exposure preferred)
• Structured and unstructured data handling

Nice-to-Have Skills

• Exposure to Generative AI / LLM-based ML workflows
• Experience with Terraform or CloudFormation
• Knowledge of automation / RPA integrations
• Domain exposure to manufacturing, supply chain, or enterprise IT

Education & Experience

• Bachelors or Master’s degree in Computer Science, Data Science, AI, or related fields
• 4–8+ years overall experience in AI/ML or data engineering roles
• Strong hands-on experience building ML solutions on AWS