5 ECS Jobs in Pune | ECS Job openings in Pune

Job Title : Senior Backend Engineer – Node.js & TypeScript

Experience : 4+ Years

Employment Type : Contract – 3 Months

Location : Pune

Work Mode : On-site

Working Hours : 03:00 PM to 11:00 PM OR 03:00 PM to 12:00 AM

Time Zone : Minimum 4-hour overlap with Eastern Time (ET)

About the Role :

We are looking for a Senior Backend Engineer to design, develop, and scale an AI-led digital platform. The role involves working closely with the Founder/CEO and technical team on architecture, product development, innovation, and end-to-end feature ownership.

Mandatory Skills :

Node.js, TypeScript, REST, GraphQL, PostgreSQL, SQL, Prisma / TypeORM / Sequelize, AWS (ECS, EC2, S3, RDS, Lambda), Airbyte / dbt / Airflow / AWS Glue, Data Warehousing, Event-Driven Architecture, Message Queues, CI/CD, Automated Testing, Backend Architecture, Scalability & Performance.

Key Responsibilities :

• Design and develop scalable backend services using Node.js and TypeScript.
• Build and optimize REST and GraphQL APIs.
• Work with PostgreSQL, SQL, and ORMs such as Prisma / TypeORM / Sequelize.
• Design data pipelines using Airbyte, dbt, Airflow, or AWS Glue.
• Manage and optimize AWS infrastructure including ECS, EC2, S3, RDS, and Lambda.
• Implement CI/CD, automated testing, event-driven architectures, and message queues.
• Optimize API performance, scalability, reliability, and data warehouse solutions.
• Own features end-to-end from design to production.
• Participate in code reviews, on-call support, and production troubleshooting.
• Collaborate with Product, QA, and Engineering teams and mentor junior developers.
• Work closely with the Founder / CEO on technical solutioning and product innovation.

Requirements :

• 4+ years of hands-on Node.js & TypeScript backend development.
• Strong experience with AWS, PostgreSQL, SQL, APIs, and backend architecture.
• Hands-on experience with data pipelines and data warehousing.
• Experience with CI/CD, automated testing, event-driven systems, and message queues.
• Strong problem-solving skills and end-to-end ownership mindset.
• Ability to work independently in a fast-paced environment.
• Good communication and collaboration skills.
• Bachelor's degree in Computer Science, IT, or a related field.

Preferred :

• Terraform / CloudFormation and Infrastructure as Code.
• Docker / Kubernetes and strong DevOps exposure.
• AWS backup / disaster recovery experience.
• Exposure to GCP / Azure.
• Startup / early-stage product development experience.

Core Responsibilities:

• The MLE will design, build, test, and deploy scalable machine learning systems, optimizing model accuracy and efficiency
• Model Development: Algorithms and architectures span traditional statistical methods to deep learning along with employing LLMs in modern frameworks.
• Data Preparation: Prepare, cleanse, and transform data for model training and evaluation.
• Algorithm Implementation: Implement and optimize machine learning algorithms and statistical models.
• System Integration: Integrate models into existing systems and workflows.
• Model Deployment: Deploy models to production environments and monitor performance.
• Collaboration: Work closely with data scientists, software engineers, and other stakeholders.
• Continuous Improvement: Identify areas for improvement in model performance and systems.

Skills:

• Programming and Software Engineering: Knowledge of software engineering best practices (version control, testing, CI/CD).
• Data Engineering: Ability to handle data pipelines, data cleaning, and feature engineering. Proficiency in SQL for data manipulation + Kafka, Chaossearch logs, etc for troubleshooting; Other tech touch points are ScyllaDB (like BigTable), OpenSearch, Neo4J graph
• Model Deployment and Monitoring: MLOps Experience in deploying ML models to production environments.
• Knowledge of model monitoring and performance evaluation.

Required experience:

• Amazon SageMaker: Deep understanding of SageMaker's capabilities for building, training, and deploying ML models; understanding of the Sagemaker pipeline with ability to analyze gaps and recommend/implement improvements
• AWS Cloud Infrastructure: Familiarity with S3, EC2, Lambda and using these services in ML workflows
• AWS data: Redshift, Glue
• Containerization and Orchestration: Understanding of Docker and Kubernetes, and their implementation within AWS (EKS, ECS)

Skills: Aws, Aws Cloud, Amazon Redshift, Eks

Must-Haves

Amazon SageMaker, AWS Cloud Infrastructure (S3, EC2, Lambda), Docker and Kubernetes (EKS, ECS), SQL, AWS data (Redshift, Glue)

Skills : Machine Learning, MLOps, AWS Cloud, Redshift OR Glue, Kubernetes, Sage maker

******

Notice period - 0 to 15 days only 

Location : Pune & Hyderabad only

MUST-HAVES: 

• Machine Learning + Aws + (EKS OR ECS OR Kubernetes) + (Redshift AND Glue) + Sage maker
• Notice period - 0 to 15 days only 
• Hybrid work mode- 3 days office, 2 days at home

SKILLS: AWS, AWS CLOUD, AMAZON REDSHIFT, EKS

ADDITIONAL GUIDELINES:

• Interview process: - 2 Technical round + 1 Client round
• 3 days in office, Hybrid model. 

CORE RESPONSIBILITIES:

• The MLE will design, build, test, and deploy scalable machine learning systems, optimizing model accuracy and efficiency
• Model Development: Algorithms and architectures span traditional statistical methods to deep learning along with employing LLMs in modern frameworks.
• Data Preparation: Prepare, cleanse, and transform data for model training and evaluation.
• Algorithm Implementation: Implement and optimize machine learning algorithms and statistical models.
• System Integration: Integrate models into existing systems and workflows.
• Model Deployment: Deploy models to production environments and monitor performance.
• Collaboration: Work closely with data scientists, software engineers, and other stakeholders.
• Continuous Improvement: Identify areas for improvement in model performance and systems.

SKILLS:

• Programming and Software Engineering: Knowledge of software engineering best practices (version control, testing, CI/CD).
• Data Engineering: Ability to handle data pipelines, data cleaning, and feature engineering. Proficiency in SQL for data manipulation + Kafka, Chaos search logs, etc. for troubleshooting; Other tech touch points are Scylla DB (like BigTable), OpenSearch, Neo4J graph
• Model Deployment and Monitoring: MLOps Experience in deploying ML models to production environments.
• Knowledge of model monitoring and performance evaluation.

REQUIRED EXPERIENCE:

• Amazon SageMaker: Deep understanding of SageMaker's capabilities for building, training, and deploying ML models; understanding of the Sage maker pipeline with ability to analyze gaps and recommend/implement improvements
• AWS Cloud Infrastructure: Familiarity with S3, EC2, Lambda and using these services in ML workflows
• AWS data: Redshift, Glue
• Containerization and Orchestration: Understanding of Docker and Kubernetes, and their implementation within AWS (EKS, ECS)

Job Details

- Job Title: ML Engineer II - Aws, Aws Cloud

- Industry: Technology

- Domain - Information technology (IT)

- Experience Required: 6-12 years

- Employment Type: Full Time

- Job Location: Pune

- CTC Range: Best in Industry

Job Description:

Core Responsibilities:

? The MLE will design, build, test, and deploy scalable machine learning systems, optimizing model accuracy and efficiency

? Model Development: Algorithms and architectures span traditional statistical methods to deep learning along with employing LLMs in modern frameworks.

? Data Preparation: Prepare, cleanse, and transform data for model training and evaluation.

? Algorithm Implementation: Implement and optimize machine learning algorithms and statistical models.

? System Integration: Integrate models into existing systems and workflows.

? Model Deployment: Deploy models to production environments and monitor performance.

? Collaboration: Work closely with data scientists, software engineers, and other stakeholders.

? Continuous Improvement: Identify areas for improvement in model performance and systems.

Skills:

? Programming and Software Engineering: Knowledge of software engineering best practices (version control, testing, CI/CD).

? Data Engineering: Ability to handle data pipelines, data cleaning, and feature engineering. Proficiency in SQL for data manipulation + Kafka, Chaossearch logs, etc for troubleshooting; Other tech touch points are ScyllaDB (like BigTable), OpenSearch, Neo4J graph

? Model Deployment and Monitoring: MLOps Experience in deploying ML models to production environments.

? Knowledge of model monitoring and performance evaluation.

Required experience:

? Amazon SageMaker: Deep understanding of SageMaker's capabilities for building, training, and deploying ML models; understanding of the Sagemaker pipeline with ability to analyze gaps and recommend/implement improvements

? AWS Cloud Infrastructure: Familiarity with S3, EC2, Lambda and using these services in

ML workflows

? AWS data: Redshift, Glue

? Containerization and Orchestration: Understanding of Docker and Kubernetes, and their implementation within AWS (EKS, ECS)

Skills: Aws, Aws Cloud, Amazon Redshift, Eks

Must-Haves

Aws, Aws Cloud, Amazon Redshift, Eks

NP: Immediate – 30 Days

SAP Basis / HANA 

• Solution Manager/FRUN Experience with detail knowledge of how SOLMAN system is integrated with Ticketing Tools. 
• Experience in Solution Manager Technical monitoring. 
• End to end monitoring for the sap system during the changes when the monitoring is not available or until the new one is in place 
• Manage the team to get the assigned task done on time adhering the SLA. 
• Align with different stakeholders to discuss the progress of multiple tasks. 
• SPOC of Alert management team. 
• Able to solve and coordinate RCA and support during critical system behaviour.
• Audit Experience. 
• Detailed Technical experience in SAP BASIS and HANA systems administrations. 
• Experience in setting up System Replications, High Availability and Disaster recovery. 
• Possess leadership qualities. 
• Proven track record of Client handling. 
• Knowledge of SAP HANA Enterprise Cloud is desirable. 
• Coach others recognize their strengths and encourage them to take ownership of their personal development. 
• Any Experience in SAP tools (SPC, LVM, etc.) will be advantage
• Ready to learn new technologies. 
• Should be ready to work in 24*7 shifts. 
• Any Experience ECS Managed Service Model would be added advantage