Data Annotator (Automotive)

Amura’s Vision 

We believe that the most under-appreciated route to releasing untapped human potential is to build a healthier body, and through which a better brain. This allows us to do more of everything that is important to each one of us.

Billions of healthier brains, sitting in healthier bodies, can take up more complex problems that defy solutions today, including many existential threats, and solve them in just a few decades.

Billions of healthier brains will make the world richer beyond what we can imagine today. The surplus wealth, combined with better human capabilities, will lead us to a new renaissance, giving us a richer and more beautiful culture.

These healthier brains will be equipped with deeper intellect, be less acrimonious, more magnanimous, and have a kinder outlook on the world, resulting in a world that is better than any previous time.

We find this vision of the future exhilarating. Our hopes and dreams are to create this future as quickly as possible and ensure that it is widely distributed and optimized to maximize all forms of human excellence. 

Role Overview 

We are looking for a highly skilled Senior DevOps Engineer (AI-Native Infrastructure & Platform Engineering) with deep expertise in AWS cloud infrastructure, automation, AI infrastructure operations, and modern DevOps/SRE practices.

This role goes beyond traditional DevOps and requires a seasoned specialist capable of building and operating AI-ready infrastructure platforms that support high-throughput APIs, LLM/AI workloads, GPU-based compute, data-intensive systems, real-time inference pipelines, and scalable ML platforms.

You will be responsible for architecting, automating, securing, and optimizing highly scalable and cost-efficient cloud environments that enable high-velocity engineering and AI teams. This is an ideal position for someone who combines technical ownership, an automation-first mindset, and a passion for developer productivity and platform reliability. 

Key Responsibilities 

Cloud Infrastructure & Platform Engineering (AWS) 

• Architect, deploy, and manage highly scalable and secure infrastructure on AWS. Design cloud platforms supporting AI/ML workloads, data pipelines, real-time APIs, and high-concurrency backend systems.
• Hands-on expertise with key AWS services including EC2, ECS/EKS, Lambda, RDS, DynamoDB, S3, VPC, CloudFront, IAM, CloudWatch, and GPU-enabled instances.
• Build and maintain Infrastructure-as-Code (IaC) using Terraform, CloudFormation, or AWS CDK.
• Design multi-AZ and multi-region architectures for high availability and disaster recovery (HA/DR).
• Build reusable platform templates and shared infrastructure modules. 

AI/ML Infrastructure & MLOps 

• Build and maintain infrastructure for LLM applications, AI inference workloads, model serving platforms, vector databases, and feature stores.
• Support GPU-based workloads and optimize compute/storage usage.
• Enable scalable deployment patterns for AI applications using Kubernetes/EKS. Collaborate with Data Science and ML Engineering teams on model deployment, training/tuning of models, CI/CD for ML systems, experiment environments, and reproducibility.
• Support orchestration and deployment of AI workflows and inference services while implementing observability and reliability for AI pipelines. 

CI/CD, Automation & Developer Productivity 

• Build and maintain CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, or AWS CodePipeline.
• Automate deployments, environment provisioning, and release workflows.
• Build self-service developer platforms, preview environments, and reusable deployment workflows to improve developer productivity.
• Implement automated patching, scaling, backups, cleanup workflows, and drift detection. 

Containers, Kubernetes & Platform Reliability

• Manage Docker-based environments, containerized applications, and optimize workloads using Kubernetes (EKS) or ECS/Fargate.
• Manage autoscaling, cluster health, node pools, ingress, service mesh, and workload isolation.
• Optimize infrastructure for performance, resilience, and cost-efficiency.
• Implement progressive deployment strategies including blue/green, canary, and rolling deployments. 

Observability, Incident Response & SRE Practices

• Implement observability stacks using CloudWatch, Prometheus, Grafana, ELK, Datadog, OpenTelemetry, or New Relic.
• Build actionable dashboards and intelligent alerting systems while defining and tracking SLIs, SLOs, and SLAs.
• Lead incident response, root cause analysis, and blameless postmortems to reduce operational toil and improve MTTR.

FinOps, Cost Governance & Security

• Continuously monitor and optimize cloud costs (compute utilization, storage lifecycle, GPU usage, and data transfer) using AWS Cost Explorer, Budgets, Trusted Advisor, CloudHealth, or Kubecost.
• Implement AWS security best practices for IAM, VPCs, security groups, NACLs, encryption, and manage secrets using KMS, SSM Parameter Store, or Vault.
• Build secure CI/CD pipelines with automated security checks, least-privilege access, audit logging, and ensure compliance readiness for ISO 27001, SOC2, and GDPR.

Collaboration, Leadership & Platform Culture

• Work closely with engineering, AI/ML, QA, product, and operations teams to drive a DevOps, SRE, GitOps, and automation-first culture.
• Mentor junior DevOps and Platform Engineers while creating and maintaining detailed runbooks, architecture diagrams, and platform documentation.

Skills & Qualifications

Must-Have:

• 7+ years of experience in DevOps, SRE, Platform Engineering, or Cloud Infrastructure Engineering.
• Strong expertise in AWS cloud architecture, services, and deep understanding of Kubernetes (EKS), containers, and cloud-native systems.
• Strong Infrastructure-as-Code expertise using Terraform, CloudFormation, or CDK. Strong Linux administration, networking, DNS, routing, and load balancing knowledge. Strong scripting/programming experience in Python, Bash, or Go (preferred). Experience with CI/CD automation, GitOps workflows, and observability platforms supporting scalable production systems.

Preferred / Nice-to-Have:

• Experience with AI/ML infrastructure, MLOps, model serving, vector databases, GPU orchestration, and inference optimization.
• Familiarity with Kafka, Redis, SQS, and event-driven systems.
• Exposure to platform engineering, internal developer platforms, and tools like ArgoCD, Flux, Helm, and OpenTelemetry.
• AWS Certifications: Solutions Architect, DevOps Engineer, or SysOps Administrator. Knowledge of distributed systems and large-scale platform operations. 

Preferred / Nice-to-Have:

• Experience with AI/ML infrastructure, MLOps, model serving, vector databases, GPU orchestration, and inference optimization.
• Familiarity with Kafka, Redis, SQS, and event-driven systems.
• Exposure to platform engineering, internal developer platforms, and tools like ArgoCD, Flux, Helm, and OpenTelemetry.
• AWS Certifications: Solutions Architect, DevOps Engineer, or SysOps Administrator. Knowledge of distributed systems and large-scale platform operations. 

Here are answers to some questions you may have

Where is your office?

Chennai (Velachery)

Work Model

Work from Office – because great stories are built in person!

Do you have an online presence?

https://amura.ai (we are @AmuraHealth on all social media)

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

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

The DevOps Engineer will play a critical role in operationalizing artificial intelligence across Bell Techlogix client environments. This role focuses on building and supporting cloud infrastructure, CI/CD pipelines, and automation frameworks that power AI and machine learning workloads. The ideal candidate has experience supporting AI platforms such as Azure AI, Azure Machine Learning, Azure OpenAI, and ServiceNow or conversational AI platforms, and understands the operational requirements of production AI systems, including reliability, scalability, and security. 

Key Responsibilities 

•Design, build, and operate cloud infrastructure and platform services that support AI and machine learning workloads in production, SLA-driven managed services environments 

•Implement CI/CD and MLOps pipelines to enable automated training, testing, deployment, and rollback of AI and ML models 

•Develop and maintain Infrastructure as Code to provision AI-ready environments consistently across dev/test/prod 

•Support AI platform operations including monitoring model health, pipeline execution, compute utilization, and data dependencies 

•Partner with Machine Learning Engineers and Data Engineers to standardize deployment patterns for AI services and LLM-based solutions 

•Enable secure and scalable AI integrations using APIs, messaging, and event-driven architectures 

•Implement observability solutions for AI platforms, including logging, metrics, alerting, and drift detection integrations 

•Troubleshoot AI platform incidents, perform root cause analysis, and implement remediation to improve reliability and automation coverage 

•Apply security best practices for AI environments including secrets management, identity and access controls, network isolation, and policy enforcement 

•Support AI-driven automation use cases across platforms such as Microsoft Copilot, ServiceNow, and conversational AI tools 

•Collaborate with service desk, security, and architecture teams to continuously improve AI service delivery and operational maturity 

Required Qualifications 

•Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience 

•5+ years of experience in DevOps, cloud engineering, or platform operations, with exposure to AI or data workloads 

•Hands-on experience with Microsoft Azure, including compute, networking, storage, and monitoring services 

•Experience building CI/CD pipelines using Azure DevOps, GitHub Actions, or similar tools 

•Working knowledge of Infrastructure as Code (Terraform and/or Bicep/ARM) 

•Scripting experience using PowerShell and/or Python 

•Experience supporting production platforms with incident management, change control, and root cause analysis 

•Understanding of cloud security fundamentals and enterprise governance requirements 

Preferred Qualifications 

•Experience with Azure Machine Learning, Azure AI Services, Azure OpenAI, or MLOps frameworks 

•Exposure to containerization and orchestration technologies (Docker, Kubernetes, AKS) 

•Experience supporting data pipelines or feature stores used by machine learning systems 

•Familiarity with ServiceNow, AI-driven ITSM workflows, or automation platforms 

•Experience with observability tools 

•Knowledge of Responsible AI, data governance, and compliance considerations for AI systems 

•Relevant certifications (Microsoft Azure Administrator, Azure DevOps Engineer, Azure AI Engineer)

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. 

Lead DevSecOps Engineer

Location: Pune, India (In-office) | Experience: 3–5 years | Type: Full-time

Apply here → https://lnk.ink/CLqe2

About FlytBase:

FlytBase is a Physical AI platform powering autonomous drones and robots across industrial sites. Our software enables 24/7 operations in critical infrastructure like solar farms, ports, oil refineries, and more.

We're building intelligent autonomy — not just automation — and security is core to that vision.

What You’ll Own

You’ll be leading and building the backbone of our AI-native drone orchestration platform — used by global industrial giants for autonomous operations.

Expect to:

• Design and manage multi-region, multi-cloud infrastructure (AWS, Kubernetes, Terraform, Docker)
• Own infrastructure provisioning through GitOps, Ansible, Helm, and IaC
• Set up observability stacks (Prometheus, Grafana) and write custom alerting rules
• Build for Zero Trust security — logs, secrets, audits, access policies
• Lead incident response, postmortems, and playbooks to reduce MTTR
• Automate and secure CI/CD pipelines with SAST, DAST, image hardening
• Script your way out of toil using Python, Bash, or LLM-based agents
• Work alongside dev, platform, and product teams to ship secure, scalable systems

What We’re Looking For:

You’ve probably done a lot of this already:

• 3–5+ years in DevOps / DevSecOps for high-availability SaaS or product infra
• Hands-on with Kubernetes, Terraform, Docker, and cloud-native tooling
• Strong in Linux internals, OS hardening, and network security
• Built and owned CI/CD pipelines, IaC, and automated releases
• Written scripts (Python/Bash) that saved your team hours
• Familiar with SOC 2, ISO 27001, threat detection, and compliance work

Bonus if you’ve:

• Played with LLMs or AI agents to streamline ops and Built bots that monitor, patch, or auto-deploy.

What It Means to Be a Flyter

• AI-native instincts: You don’t just use AI — you think in it. Your terminal window has a co-pilot.
• Ownership without oversight: You own outcomes, not tasks. No one micromanages you here.
• Joy in complexity: Security + infra + scale = your happy place.
• Radical candor: You give and receive sharp feedback early — and grow faster because of it.
• Loops over lines: we prioritize continuous feedback, iteration, and learning over one-way execution or rigid, linear planning.
• H3: Happy. Healthy. High-Performing. We believe long-term performance stems from an environment where you feel emotionally fulfilled, physically well, and deeply motivated.
• Systems > Heroics: We value well-designed, repeatable systems over last-minute firefighting or one-off effort.

Perks:

▪ Unlimited leave & flexible hours

▪ Top-tier health coverage

▪ Budget for AI tools, courses

▪ International deployments

▪ ESOPs and high-agency team culture

Apply Here- https://lnk.ink/CLqe2

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

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