Automation Tester

Review Criteria:

• Strong MLOps profile
• 8+ years of DevOps experience and 4+ years in MLOps / ML pipeline automation and production deployments
• 4+ years hands-on experience in Apache Airflow / MWAA managing workflow orchestration in production
• 4+ years hands-on experience in Apache Spark (EMR / Glue / managed or self-hosted) for distributed computation
• Must have strong hands-on experience across key AWS services including EKS/ECS/Fargate, Lambda, Kinesis, Athena/Redshift, S3, and CloudWatch
• Must have hands-on Python for pipeline & automation development
• 4+ years of experience in AWS cloud, with recent companies
• (Company) - Product companies preferred; Exception for service company candidates with strong MLOps + AWS depth

Preferred:

• Hands-on in Docker deployments for ML workflows on EKS / ECS
• Experience with ML observability (data drift / model drift / performance monitoring / alerting) using CloudWatch / Grafana / Prometheus / OpenSearch.
• Experience with CI / CD / CT using GitHub Actions / Jenkins.
• Experience with JupyterHub/Notebooks, Linux, scripting, and metadata tracking for ML lifecycle.
• Understanding of ML frameworks (TensorFlow / PyTorch) for deployment scenarios.

Job Specific Criteria:

• CV Attachment is mandatory
• Please provide CTC Breakup (Fixed + Variable)?
• Are you okay for F2F round?
• Have candidate filled the google form?

Role & Responsibilities:

We are looking for a Senior MLOps Engineer with 8+ years of experience building and managing production-grade ML platforms and pipelines. The ideal candidate will have strong expertise across AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows. You will work closely with data science, data engineering, and platform teams to operationalize and scale ML models in production.

Key Responsibilities:

• Design and manage cloud-native ML platforms supporting training, inference, and model lifecycle automation.
• Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue).
• Containerize and deploy ML workloads using Docker, EKS, ECS/Fargate, and Lambda.
• Develop CI/CT/CD pipelines integrating model validation, automated training, testing, and deployment.
• Implement ML observability: model drift, data drift, performance monitoring, and alerting using CloudWatch, Grafana, Prometheus.
• Ensure data governance, versioning, metadata tracking, reproducibility, and secure data pipelines.
• Collaborate with data scientists to productionize notebooks, experiments, and model deployments.

Ideal Candidate:

• 8+ years in MLOps/DevOps with strong ML pipeline experience.
• Strong hands-on experience with AWS:
• Compute/Orchestration: EKS, ECS, EC2, Lambda
• Data: EMR, Glue, S3, Redshift, RDS, Athena, Kinesis
• Workflow: MWAA/Airflow, Step Functions
• Monitoring: CloudWatch, OpenSearch, Grafana
• Strong Python skills and familiarity with ML frameworks (TensorFlow/PyTorch/Scikit-learn).
• Expertise with Docker, Kubernetes, Git, CI/CD tools (GitHub Actions/Jenkins).
• Strong Linux, scripting, and troubleshooting skills.
• Experience enabling reproducible ML environments using Jupyter Hub and containerized development workflows.

Education:

• Master’s degree in computer science, Machine Learning, Data Engineering, or related field. 

Job Description:

We are looking for a skilled Go (Golang) Developer to build scalable backend services and APIs. The candidate should have strong experience in microservices architecture and cloud-based development.

Key Skills:

• Golang / Go Programming
• REST API Development
• Microservices Architecture
• SQL / NoSQL Databases
• Docker & CI/CD
• Git

Good to Have:

• Experience with frameworks like Gin or Echo
• Cloud platforms (AWS / GCP)

Responsibilities:

• Develop and maintain backend services using Golang
• Build scalable APIs and microservices
• Collaborate with cross-functional teams to deliver high-quality applications
• Write clean, efficient, and well-tested code

• Experience with Infrastructure-as-Code tools(IaS) like Terraform and Cloud Formation.
• Proficiency in cloud-native technologies and architectures (Docker/ Kubernetes), Ci/CD pipelines.
• Good experience in Javascript.
• Expertise in Linux / Windows environment.
• Good Experience in Scripting languages like PowerShell / Bash/ Python.
• Proficiency in revision control and DevOps best practices like Git

Responsibilities

• End-to-End Embedded/DSP system design.

• Complex logic implementation using RTL coding techniques (preferably VHDL and HLS)

• Able to implement various complex algorithms and curve fitting/Neural network based algorithms inside the FPGA hardware.

• Writing modular RTL codes and design techniques using IPs.

• Simulating the design at various stages of development.

• CDC, STA, area/power optimizations, floor planning, linting.

• Interfacing high speed ADCs/DACs using state of the art data transfer links to FPGA.

• Using DDR RAM and SG-DMA for low latency, high-speed data transfer.

• Familiarity with high-speed communication systems viz. USB 3, PCIe, Gbe, 10 Gbe, 100

Gbe etc.

• Linux device driver development.

Skills

• VHDL/Verilog, C/C++, familiar with Xilinx development tools.

• Design knowledge of PCIe, LVDS, SPI, AXI, USB etc. interfaces.

• Ability to design using primitives instead of inferred design from RTL.

• Embedded linux device drivers, petalinux, exposure to yocto project.

• Exposure to various compression algorithms.

Desired Qualification/Experience

• Talent and zeal to work with new challenging system designs.

• Thorough understanding of digital systems.

• Hands-on experience on the RTL design, preferably with a repository of previous

projects

Job Description:

Proven experience as a Java Developer with a strong understanding of Java and its ecosystems.

Proficient in Spring Framework (Spring Boot, Spring MVC, Spring Data).

Experience with web application development using technologies such as HTML, CSS, and JavaScript.

Knowledge of relational databases (e.g., MySQL, PostgreSQL) and ORM frameworks (e.g., Hibernate).

Familiarity with build tools such as Maven or Gradle.

Understanding of software development life cycle (SDLC) and agile methodologies.

Excellent problem-solving and debugging skills.

Experience with microservices architecture.

Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).

Candidate should also have some App development experience in cloud infra. Like familiarity with Docker, Helm Charts, Kubernetes or AWS/Azure k8s infra.

Candidate should have some App development experience in at least one No-SQL Database also (like MongoDB or Cassandra or Redis) apart from RDBMS.