Data Annotator (Automotive)

About the Role

We are looking for passionate and driven interns across multiple technology domains including Frontend Development, Backend Development, DevOps, AI/ML, and Data Engineering. This internship offers hands-on experience in real-world projects, collaboration with cross-functional teams, and exposure to modern tools and technologies. 

Domains & Responsibilities 

Frontend Development 

• Build responsive and user-friendly web interfaces  
• Translate UI/UX designs into functional applications  
• Optimize performance and ensure cross-browser compatibility  

 Backend Development 

• Develop APIs and server-side logic  
• Work with databases and data storage solutions  
• Ensure application security and performance  

 DevOps 

• Assist in CI/CD pipeline setup and automation  
• Manage deployments and cloud infrastructure  
• Monitor system performance and reliability  

 AI / Machine Learning 

• Develop and train ML models  
• Work on NLP, automation, or AI-driven features  
• Analyze datasets and evaluate model performance  

 Data Engineering 

• Build and maintain data pipelines (ETL/ELT)  
• Ensure data quality and availability  
• Work with large datasets and optimize data workflows  

 Required Skills (Any Domain) 

• Frontend: HTML, CSS, JavaScript, React/Vue/Angular  
• Backend: Node.js / Python / Java / PHP, APIs, databases  
• DevOps: Linux, Git, CI/CD basics, cloud fundamentals  
• AI/ML: Python, ML basics, TensorFlow/PyTorch/Scikit-learn  
• Data Engineering: SQL, Python, data processing concepts  

 Good to Have 

• Knowledge of Git and version control  
• Basic understanding of cloud platforms (AWS/Azure/GCP)  
• Problem-solving mindset and willingness to learn  
• Exposure to real-world or academic projects  

 Who Should Apply 

• Students or recent graduates in Computer Science, IT, or related fields  
• Candidates with strong interest in any of the above domains  
• Self-learners with project experience are highly encouraged  

 Internship Details 

• Duration: 3–6 months  
• Mode: Remote  
• Certificate + PPO (Pre-Placement Offer) based on performance  

 What You’ll Gain 

• Hands-on experience with real projects  
• Mentorship from experienced professionals  
• Exposure to industry tools and workflows  
• Opportunity to convert to a full-time role 

We are looking for a highly skilled and experienced Senior AIOps / MLOps Engineer with strong expertise in Azure Cloud, automation, platform engineering, CI/CD, observability, and enterprise-scale cloud operations.

The ideal candidate should have hands-on experience in designing, implementing, and managing modern cloud-native platforms with focus on AI/ML operationalization, DevOps automation, monitoring, reliability, and infrastructure modernization.

Required Experience

• 6 – 10 Years of overall IT experience
• Strong experience in AIOps / MLOps / DevOps engineering
• Hands-on enterprise experience in Azure Cloud platform engineering

Key Responsibilities

AIOps / MLOps

• Design and implement scalable enterprise-grade AIOps and MLOps platforms across cloud environments.
• Ensure AI platform reliability, governance, security, and model performance optimization.
• Implement LLM/AI model versioning, experiment tracking, drift detection, observability, and operational health monitoring frameworks.
• Collaborate with Data Science, DevOps, Cloud, and Application teams to accelerate AI/ML adoption and platform modernization.
• Develop automation frameworks for AI/ML pipelines, infrastructure provisioning, and operational workflows.
• Lead continuous improvement, automation, and standardization efforts across AI/ML operational ecosystems
• Mentor engineering teams and promote AIOps/MLOps best practices, innovation, and engineering excellence
• Strong Knowledge on embeddings, tokenization, vector databases, and AI/ML model training concepts

Preferred Skills

• Python, MLflow, Model Registry, Experiment Tracking
• Azure DevOps & Azure Cloud
• Azure Machine Learning
• LLMOps / Generative AI operationalization
• AI model deployment and lifecycle management
• AI Gateway and Model Serving architectures
• Azure OpenAI & Azure AI Foundry
• MCP Server implementation and configuration  
• CI/CD Automation & AKS

Soft Skills

• Strong communication and stakeholder management
• Good troubleshooting and problem-solving skills
• Ability to work independently and drive ownership
• Strong collaboration and documentation skills

Review Criteria

• Strong DevOps /Cloud Engineer Profiles
• Must have 3+ years of experience as a DevOps / Cloud Engineer
• Must have strong expertise in cloud platforms – AWS / Azure / GCP (any one or more)
• Must have strong hands-on experience in Linux administration and system management
• Must have hands-on experience with containerization and orchestration tools such as Docker and Kubernetes
• Must have experience in building and optimizing CI/CD pipelines using tools like GitHub Actions, GitLab CI, or Jenkins
• Must have hands-on experience with Infrastructure-as-Code tools such as Terraform, Ansible, or CloudFormation
• Must be proficient in scripting languages such as Python or Bash for automation
• Must have experience with monitoring and alerting tools like Prometheus, Grafana, ELK, or CloudWatch
• Top tier Product-based company (B2B Enterprise SaaS preferred)

Preferred

• Experience in multi-tenant SaaS infrastructure scaling.
• Exposure to AI/ML pipeline deployments or iPaaS / reverse ETL connectors.

Role & Responsibilities

We are seeking a DevOps Engineer to design, build, and maintain scalable, secure, and resilient infrastructure for our SaaS platform and AI-driven products. The role will focus on cloud infrastructure, CI/CD pipelines, container orchestration, monitoring, and security automation, enabling rapid and reliable software delivery.

Key Responsibilities:

• Design, implement, and manage cloud-native infrastructure (AWS/Azure/GCP).
• Build and optimize CI/CD pipelines to support rapid release cycles.
• Manage containerization & orchestration (Docker, Kubernetes).
• Own infrastructure-as-code (Terraform, Ansible, CloudFormation).
• Set up and maintain monitoring & alerting frameworks (Prometheus, Grafana, ELK, etc.).
• Drive cloud security automation (IAM, SSL, secrets management).
• Partner with engineering teams to embed DevOps into SDLC.
• Troubleshoot production issues and drive incident response.
• Support multi-tenant SaaS scaling strategies.

Ideal Candidate

• 3–6 years' experience as DevOps/Cloud Engineer in SaaS or enterprise environments.
• Strong expertise in AWS, Azure, or GCP.
• Strong expertise in LINUX Administration.
• Hands-on with Kubernetes, Docker, CI/CD tools (GitHub Actions, GitLab, Jenkins).
• Proficient in Terraform/Ansible/CloudFormation.
• Strong scripting skills (Python, Bash).
• Experience with monitoring stacks (Prometheus, Grafana, ELK, CloudWatch).
• Strong grasp of cloud security best practices.

ROLE & RESPONSIBILITIES:

We are hiring a Senior DevSecOps / Security Engineer with 8+ years of experience securing AWS cloud, on-prem infrastructure, DevOps platforms, MLOps environments, CI/CD pipelines, container orchestration, and data/ML platforms. This role is responsible for creating and maintaining a unified security posture across all systems used by DevOps and MLOps teams — including AWS, Kubernetes, EMR, MWAA, Spark, Docker, GitOps, observability tools, and network infrastructure.

KEY RESPONSIBILITIES:

1.     Cloud Security (AWS)-

• Secure all AWS resources consumed by DevOps/MLOps/Data Science: EC2, EKS, ECS, EMR, MWAA, S3, RDS, Redshift, Lambda, CloudFront, Glue, Athena, Kinesis, Transit Gateway, VPC Peering.
• Implement IAM least privilege, SCPs, KMS, Secrets Manager, SSO & identity governance.
• Configure AWS-native security: WAF, Shield, GuardDuty, Inspector, Macie, CloudTrail, Config, Security Hub.
• Harden VPC architecture, subnets, routing, SG/NACLs, multi-account environments.
• Ensure encryption of data at rest/in transit across all cloud services.

2.     DevOps Security (IaC, CI/CD, Kubernetes, Linux)-

Infrastructure as Code & Automation Security:

• Secure Terraform, CloudFormation, Ansible with policy-as-code (OPA, Checkov, tfsec).
• Enforce misconfiguration scanning and automated remediation.

CI/CD Security:

• Secure Jenkins, GitHub, GitLab pipelines with SAST, DAST, SCA, secrets scanning, image scanning.
• Implement secure build, artifact signing, and deployment workflows.

Containers & Kubernetes:

• Harden Docker images, private registries, runtime policies.
• Enforce EKS security: RBAC, IRSA, PSP/PSS, network policies, runtime monitoring.
• Apply CIS Benchmarks for Kubernetes and Linux.

Monitoring & Reliability:

• Secure observability stack: Grafana, CloudWatch, logging, alerting, anomaly detection.
• Ensure audit logging across cloud/platform layers.

3.     MLOps Security (Airflow, EMR, Spark, Data Platforms, ML Pipelines)-

Pipeline & Workflow Security:

• Secure Airflow/MWAA connections, secrets, DAGs, execution environments.
• Harden EMR, Spark jobs, Glue jobs, IAM roles, S3 buckets, encryption, and access policies.

ML Platform Security:

• Secure Jupyter/JupyterHub environments, containerized ML workspaces, and experiment tracking systems.
• Control model access, artifact protection, model registry security, and ML metadata integrity.

Data Security:

• Secure ETL/ML data flows across S3, Redshift, RDS, Glue, Kinesis.
• Enforce data versioning security, lineage tracking, PII protection, and access governance.

ML Observability:

• Implement drift detection (data drift/model drift), feature monitoring, audit logging.
• Integrate ML monitoring with Grafana/Prometheus/CloudWatch.

4.     Network & Endpoint Security-

• Manage firewall policies, VPN, IDS/IPS, endpoint protection, secure LAN/WAN, Zero Trust principles.
• Conduct vulnerability assessments, penetration test coordination, and network segmentation.
• Secure remote workforce connectivity and internal office networks.

5.     Threat Detection, Incident Response & Compliance-

• Centralize log management (CloudWatch, OpenSearch/ELK, SIEM).
• Build security alerts, automated threat detection, and incident workflows.
• Lead incident containment, forensics, RCA, and remediation.
• Ensure compliance with ISO 27001, SOC 2, GDPR, HIPAA (as applicable).
• Maintain security policies, procedures, RRPs (Runbooks), and audits.

IDEAL CANDIDATE:

• 8+ years in DevSecOps, Cloud Security, Platform Security, or equivalent.
• Proven ability securing AWS cloud ecosystems (IAM, EKS, EMR, MWAA, VPC, WAF, GuardDuty, KMS, Inspector, Macie).
• Strong hands-on experience with Docker, Kubernetes (EKS), CI/CD tools, and Infrastructure-as-Code.
• Experience securing ML platforms, data pipelines, and MLOps systems (Airflow/MWAA, Spark/EMR).
• Strong Linux security (CIS hardening, auditing, intrusion detection).
• Proficiency in Python, Bash, and automation/scripting.
• Excellent knowledge of SIEM, observability, threat detection, monitoring systems.
• Understanding of microservices, API security, serverless security.
• Strong understanding of vulnerability management, penetration testing practices, and remediation plans.

EDUCATION:

• Master’s degree in Cybersecurity, Computer Science, Information Technology, or related field.
• Relevant certifications (AWS Security Specialty, CISSP, CEH, CKA/CKS) are a plus.

PERKS, BENEFITS AND WORK CULTURE:

• Competitive Salary Package
• Generous Leave Policy
• Flexible Working Hours
• Performance-Based Bonuses
• Health Care Benefits

Why this role exists

Our infrastructure footprint is growing faster than our headcount, and we believe most of that

gap should be closed by automation and AI agents — not by hiring more humans to do toil. We

need someone early in their career who treats manual work as a bug, ships scripts and agents

instead of tickets, and wants to grow into deeper ownership over the next two years.

You will not be the most senior person on the team. You will be the one who multiplies the team.

What you'll own

In your first 1 months

• Take ownership of one slice of our CI/CD pipeline and make it measurably

faster, more reliable, or cheaper. We expect a number on a dashboard to move.

• Build at least three internal automations that replace manual ops toil —

using AI agents (Claude Code, agentic CLIs, scripted LLM workflows) as your force

multiplier.

• Be the first responder for a defined set of alerts. Write the runbooks. Drive

the alert volume down.

• Support senior engineers on AI/ML infrastructure (GPU nodes, inference

services, model deployment) — observe, document, and gradually take on contained

changes under review.

By 3 months you should be

• The go-to person for at least two production systems.

• Shipping routine infrastructure changes without needing senior review.

• Treating "manual" as a code smell.

Required (we will reject without these)

• 0–3 years hands-on experience with one major cloud (AWS, GCP, or

Azure — one is fine, depth beats breadth).

• Fluent in Linux command line, bash, and at least one scripting language

(Python or Go preferred).

• Have shipped something to production that real users hit. A side project

counts; a graded coursework lab does not.

• Comfortable with Docker — you can explain what an image vs. a

container is and why it matters.

• Working knowledge of networking fundamentals: DNS, HTTP/HTTPS,

TLS, ports, basic subnets — enough to debug "it works on my machine."

• Git fluency: branches, merges, rebases, conflict resolution.

• CI/CD pipelines — you have authored or substantially modified pipelines

in GitHub Actions, GitLab CI, ArgoCD, Jenkins, or similar. Not just "I clicked Re-run."

• Kubernetes basics — kubectl for real work, can read pod logs,

understand deployments and services, can debug a CrashLoopBackOff without

panicking. You do not need to have run a cluster; you do need to have lived inside one.

• Active user of AI coding agents (Claude Code, Cursor, Copilot, agentic

CLIs, etc.). You should be able to walk us through specific tasks where they made you

faster, and specific tasks where they failed you and how you noticed. "I have tried it" is

not enough.

Bonus (real plus, not required)

• Infrastructure as Code: Terraform, Pulumi, or Ansible.

• Observability: Prometheus/Grafana, Datadog, OpenTelemetry, any APM.

• Have built or extended an LLM-based agent — a custom MCP server, a

scripted multi-step workflow, an internal tool that calls models in a loop. Anything beyond

chat-with-Claude.

• Exposure to GPU workloads, model serving (vLLM, Triton, TGI, etc.), or

ML pipelines.

What we don't care about

• Whether your degree is in CS — or whether you have a degree at all.

• Brand-name companies on your resume.

• Certifications. They are fine. They do not substitute for having shipped.

How we work

• We default to automation. If you do something manually twice, the third

time you script it or hand it to an agent.

• AI agents are part of the workflow, not a novelty. Expect interview

questions about exactly how you use them — and where you have caught them being

wrong.

• Small, reversible changes beat big-bang rollouts.

• Postmortems are blameless and written down.

• We push back on each other. If you only execute, you will be unhappy

here.

How to apply

Send:

• Your resume.

• A short note (≤200 words) describing one infra or automation problem you

solved, and how AI agents factored in — or did not, and why. We read these. Generic

notes get rejected.

Internal note — delete before posting externally

• Comp band, location policy, team name, and reporting line marked

[CONFIRM] need to be filled in before this goes external.

• The Required list is intentionally tight: CI/CD and Kubernetes basics

promoted from bonus. Expect this to filter ~80% of typical junior DevOps applicants. The

remaining pool will skew toward people who have actually shipped infra at a startup, not

bootcamp grads or pure cloud-cert holders.

• IaC, observability, agent-building, and GPU/ML serving stay as bonus.

Promoting any of these to required at 0–3 yrs collapses the pool to near-zero or forces

hiring senior people at junior comp. If you want IaC required, re-level this to mid (3–5

yrs) and raise the band.

• Screening implication: the resume screen should explicitly check for

CI/CD pipeline authorship and any K8s-touching production work. If neither is on the

resume, reject at screen. Do not waste interview slots.

• Pipeline watch: if fewer than ~15 qualified resumes after 2 weeks of

active sourcing, the first thing to relax is the AI-agent-fluency bar (move to bonus and

screen for it in interview instead). Do not relax the "shipped to production" requirement

— that is the load-bearing filter.

Job Title: Senior AIML Engineer – Immediate Joiner (AdTech)

Location: Pune – Onsite

About Us:

We are a cutting-edge technology company at the forefront of digital transformation, building innovative AI and machine learning solutions for the digital advertising industry. Join us in shaping the future of AdTech!

Role Overview:

We are looking for a highly skilled Senior AIML Engineer with AdTech experience to develop intelligent algorithms and predictive models that optimize digital advertising performance. Immediate joiners preferred.

Key Responsibilities:

• Design and implement AIML models for real-time ad optimization, audience targeting, and campaign performance analysis.
• Collaborate with data scientists and engineers to build scalable AI-driven solutions.
• Analyze large volumes of data to extract meaningful insights and improve ad performance.
• Develop and deploy machine learning pipelines for automated decision-making.
• Stay updated on the latest AI/ML trends and technologies to drive continuous innovation.
• Optimize existing models for speed, scalability, and accuracy.
• Work closely with product managers to align AI solutions with business goals.

Requirements:

• Minimum 4-6 years of experience in AIML, with a focus on AdTech (Mandatory).
• Strong programming skills in Python, R, or similar languages.
• Hands-on experience with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Expertise in data processing and real-time analytics.
• Strong understanding of digital advertising, programmatic platforms, and ad server technology.
• Excellent problem-solving and analytical skills.
• Immediate joiners preferred.

Preferred Skills:

• Knowledge of big data technologies like Spark, Hadoop, or Kafka.
• Experience with cloud platforms like AWS, GCP, or Azure.
• Familiarity with MLOps practices and tools.

How to Apply:

If you are a passionate AIML engineer with AdTech experience and can join immediately, we want to hear from you. Share your resume and a brief note on your relevant experience.

Join us in building the future of AI-driven digital advertising!

Roles and Responsibilities: 

▪ Data Pipeline Development: Build, deploy, and maintain efficient ETL/ELT pipelines using Azure 

Data Factory, Data Factory & Azure Synapse Analytics. 

▪ We are only looking for senior candidates with over 5 yrs of relevant exp with ample client 

facing exp.

· Finance/Insurance experience is also a must. 

▪ Data Modelling & Warehousing: Design and optimize data models, warehouses, and lakes for 

structured/unstructured data. 

▪ SQL & Query Optimization: Write complex SQL queries, optimize performance, and manage 

databases. · Python Automation: Develop scripts for data processing, automation, and 

integration using Python (Pandas, NumPy). 

Technical Skills: 

▪ Cloud Technologies: Azure Synapse Analytics, Azure Fabric, Azure Databricks and AWS(good to 

have) 

▪ Knowledge of Python, Pyspark, SQL, ETL concepts 

▪ Good understanding of Insurance Operations and KPI reporting is an advantage. 

Objectives :

• Building and setting up new development tools and infrastructure
• Working on ways to automate and improve development and release processes
• Testing code written by others and analyzing results
• Ensuring that systems are safe and secure against cybersecurity threats
• Identifying technical problems and developing software updates and ‘fixes’
• Working with software developers and software engineers to ensure that development follows established processes and works as intended
• Planning out projects and being involved in project management decisions

Daily and Monthly Responsibilities :

• Deploy updates and fixes
• Build tools to reduce occurrences of errors and improve customer experience
• Develop software to integrate with internal back-end systems
• Perform root cause analysis for production errors
• Investigate and resolve technical issues
• Develop scripts to automate visualization
• Design procedures for system troubleshooting and maintenance

Skills and Qualifications :

• Degree in Computer Science or Software Engineering or BSc in Computer Science, Engineering or relevant field
• 3+ years of experience as a DevOps Engineer or similar software engineering role
• Proficient with git and git workflows
• Good logical skills and knowledge of programming concepts(OOPS,Data Structures)
• Working knowledge of databases and SQL
• Problem-solving attitude
• Collaborative team spirit

About the job

Our goal

We are reinventing the future of MLOps. Censius Observability platform enables businesses to gain greater visibility into how their AI makes decisions to understand it better. We enable explanations of predictions, continuous monitoring of drifts, and assessing fairness in the real world. (TLDR build the best ML monitoring tool)

The culture

We believe in constantly iterating and improving our team culture, just like our product. We have found a good balance between async and sync work default is still Notion docs over meetings, but at the same time, we recognize that as an early-stage startup brainstorming together over calls leads to results faster. If you enjoy taking ownership, moving quickly, and writing docs, you will fit right in.

The role:

Our engineering team is growing and we are looking to bring on board a senior software engineer who can help us transition to the next phase of the company. As we roll out our platform to customers, you will be pivotal in refining our system architecture, ensuring the various tech stacks play well with each other, and smoothening the DevOps process.

On the platform, we use Python (ML-related jobs), Golang (core infrastructure), and NodeJS (user-facing). The platform is 100% cloud-native and we use Envoy as a proxy (eventually will lead to service-mesh architecture).

By joining our team, you will get the exposure to working across a swath of modern technologies while building an enterprise-grade ML platform in the most promising area.

Responsibilities

• Be the bridge between engineering and product teams. Understand long-term product roadmap and architect a system design that will scale with our plans.
• Take ownership of converting product insights into detailed engineering requirements. Break these down into smaller tasks and work with the team to plan and execute sprints.
• Author high-quality, highly-performance, and unit-tested code running on a distributed environment using containers.
• Continually evaluate and improve DevOps processes for a cloud-native codebase.
• Review PRs, mentor others and proactively take initiatives to improve our team's shipping velocity.
• Leverage your industry experience to champion engineering best practices within the organization.

Qualifications

Work Experience

• 3+ years of industry experience (2+ years in a senior engineering role) preferably with some exposure in leading remote development teams in the past.
• Proven track record building large-scale, high-throughput, low-latency production systems with at least 3+ years working with customers, architecting solutions, and delivering end-to-end products.
• Fluency in writing production-grade Go or Python in a microservice architecture with containers/VMs for over 3+ years.
• 3+ years of DevOps experience (Kubernetes, Docker, Helm and public cloud APIs)
• Worked with relational (SQL) as well as non-relational databases (Mongo or Couch) in a production environment.
• (Bonus: worked with big data in data lakes/warehouses).
• (Bonus: built an end-to-end ML pipeline)

Skills

• Strong documentation skills. As a remote team, we heavily rely on elaborate documentation for everything we are working on.
• Ability to motivate, mentor, and lead others (we have a flat team structure, but the team would rely upon you to make important decisions)
• Strong independent contributor as well as a team player.
• Working knowledge of ML and familiarity with concepts of MLOps

Benefits

• Competitive Salary
• Work Remotely
• Health insurance
• Unlimited Time Off
• Support for continual learning (free books and online courses)
• Reimbursement for streaming services (think Netflix)
• Reimbursement for gym or physical activity of your choice
• Flex hours
• Leveling Up Opportunities

You will excel in this role if

• You have a product mindset. You understand, care about, and can relate to our customers.
• You take ownership, collaborate, and follow through to the very end.
• You love solving difficult problems, stand your ground, and get what you want from engineers.
• Resonate with our core values of innovation, curiosity, accountability, trust, fun, and social good.

At Karza technologies, we take pride in building one of the most comprehensive digital onboarding & due-diligence platforms by profiling millions of entities and trillions of associations amongst them using data collated from more than 700 publicly available government sources. Primarily in the B2B Fintech Enterprise space, we are headquartered in Mumbai in Lower Parel with 100+ strong workforce. We are truly furthering the cause of Digital India by providing the entire BFSI ecosystem with tech products and services that aid onboarding customers, automating processes and mitigating risks seamlessly, in real-time and at fraction of the current cost.

A few recognitions:

• Recognized as Top25 startups in India to work with 2019 by LinkedIn
• Winner of HDFC Bank's Digital Innovation Summit 2020
• Super Winners (Won every category) at Tecnoviti 2020 by Banking Frontiers
• Winner of Amazon AI Award 2019 for Fintech
• Winner of FinTech Spot Pitches at Fintegrate Zone 2018 held at BSE
• Winner of FinShare 2018 challenge held by ShareKhan
• Only startup in Yes Bank Global Fintech Accelerator to win the account during the Cohort
• 2nd place Citi India FinTech Challenge 2018 by Citibank
• Top 3 in Viacom18's Startup Engagement Programme VStEP

What your average day would look like:

• Deploy and maintain mission-critical information extraction, analysis, and management systems
• Manage low cost, scalable streaming data pipelines
• Provide direct and responsive support for urgent production issues
• Contribute ideas towards secure and reliable Cloud architecture
• Use open source technologies and tools to accomplish specific use cases encountered within the project
• Use coding languages or scripting methodologies to solve automation problems
• Collaborate with others on the project to brainstorm about the best way to tackle a complex infrastructure, security, or deployment problem
• Identify processes and practices to streamline development & deployment to minimize downtime and maximize turnaround time

What you need to work with us:

• Proficiency in at least one of the general-purpose programming languages like Python, Java, etc.
• Experience in managing the IAAS and PAAS components on popular public Cloud Service Providers like AWS, Azure, GCP etc.
• Proficiency in Unix Operating systems and comfortable with Networking concepts
• Experience with developing/deploying a scalable system
• Experience with the Distributed Database & Message Queues (like Cassandra, ElasticSearch, MongoDB, Kafka, etc.)
• Experience in managing Hadoop clusters
• Understanding of containers and have managed them in production using container orchestration services.
• Solid understanding of data structures and algorithms.
• Applied exposure to continuous delivery pipelines (CI/CD).
• Keen interest and proven track record in automation and cost optimization.

Experience:

• 1-4 years of relevant experience
• BE in Computer Science / Information Technology