Data Annotator (Automotive)

Supercharge Your Career as a AI DevOps Engineer at Technoidentity!

At Technoidentity, we're a Data & AI product engineering company with over 15 years of expertise in building durable digital products, intelligent enterprise solutions, and scalable Data & AI platforms. As we continue expanding globally, it's the perfect time to join our team of tech innovators and make a lasting impact.

What’s in it for You?

We are looking for an AI DevOps Engineer with 0–3 years of experience who is passionate about AI, Cloud, DevOps, and Automation. The role involves building, deploying, and managing AI-powered applications, LLM solutions, and cloud-native platforms while ensuring reliability, scalability, security, and observability.

What Will You Be Doing?

• Develop and deploy AI/ML and Generative AI solutions using Python.
• Build applications leveraging LLMs, RAG, and AI agents.
• Create and maintain CI/CD pipelines for AI applications.
• Deploy and manage workloads using Docker and Kubernetes.
• Support cloud platforms (AWS, Azure, or GCP).
• Implement Infrastructure as Code (Terraform) and automation workflows.
• Monitor applications using observability tools such as Prometheus, Grafana, and logging platforms.
• Collaborate with engineering teams to ensure system reliability, performance, and security.
• Contribute to MLOps practices, AI accelerators, and reusable frameworks.

Requirements

What Makes You the Perfect Fit?

• Python programming (mandatory)
• Understanding of Machine Learning, LLMs, Prompt Engineering, and RAG
• Experience with OpenAI, LangChain, LlamaIndex, or Hugging Face
• Docker, Kubernetes, Git, and CI/CD tools
• AWS, Azure, or GCP
• PostgreSQL; MongoDB and Vector Databases are a plus
• Basic knowledge of MLOps, Terraform, and workflow orchestration tools (Airflow/Temporal)
• Familiarity with observability and monitoring tools

Qualifications

• Bachelor's degree in Computer Science, AI, Data Science, IT, or related field
• 0–3 years of experience in AI/ML, Software Engineering, Cloud, DevOps, or related areas

Nice to Have

• Experience with Agentic AI frameworks
• Knowledge of MLOps and AI platform operations
• Exposure to enterprise-grade monitoring, reliability engineering, and security best practices

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 

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.

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. 

Key Responsibilities:-

• Collaborate with Data Scientists to test and scale new algorithms through pilots and later industrialize the solutions at scale to the comprehensive fashion network of the Group

• Influence, build and maintain the large-scale data infrastructure required for the AI projects, and integrate with external IT infrastructure/service to provide an e2e solution

• Leverage an understanding of software architecture and software design patterns to write scalable, maintainable, well-designed and future-proof code

• Design, develop and maintain the framework for the analytical pipeline

• Develop common components to address pain points in machine learning projects, like model lifecycle management, feature store and data quality evaluation

• Provide input and help implement framework and tools to improve data quality

• Work in cross-functional agile teams of highly skilled software/machine learning engineers, data scientists, designers, product managers and others to build the AI ecosystem within the Group

• Deliver on time, demonstrating a strong commitment to deliver on the team mission and agreed backlog

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