Agile Project Manager

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

Job Details

- Job Title: Lead DevOps Engineer

- Industry: Consumer Internet, Technology & Travel and Tourism Platform

- Function - IT

- Experience Required: 7-10 years

- Employment Type: Full Time

- Job Location: Bengaluru

- CTC Range: Best in Industry

Criteria:

• Strong Lead DevOps / Infrastructure Engineer Profiles.
• Must have 7+ years of hands-on experience working as a DevOps / Infrastructure Engineer.
• Candidate’s current title must be Lead DevOps Engineer (or equivalent Lead role) in the current organization
• Must have minimum 2+ years of team management / technical leadership experience, including mentoring engineers, driving infrastructure decisions, or leading DevOps initiatives.
• Must have strong hands-on experience with Kubernetes (container orchestration) including deployment, scaling, and cluster management.
• Must have experience with Infrastructure as Code (IaC) tools such as Terraform, Ansible, Chef, or Puppet.
• Must have strong scripting and automation experience using Python, Go, Bash, or similar scripting languages.
• Must have working experience with distributed databases or data systems such as MongoDB, Redis, Cassandra, Elasticsearch, or Puppet.
• Must have strong hands-on experience in Observability & Monitoring, CI/CD architecture, and Networking concepts in production environments.
• (Company) – Must be from B2C Product Companies only.
• (Education) – B.E/ B.Tech

Preferred

• Experience working in microservices architecture and event-driven systems.
• Exposure to cloud infrastructure, scalability, reliability, and cost optimization practices.
• (Skills) – Understanding of programming languages such as Go, Python, or Java.
• (Environment) – Experience working in high-growth startup or large-scale production environments.

Job Description 

As a DevOps Engineer, you will be working on building and operating infrastructure at scale, designing and implementing a variety of tools to enable product teams to build and deploy their services independently, improving observability across the board, and designing for security, resiliency, availability, and stability. If the prospect of ensuring system reliability at scale and exploring cutting-edge technology to solve problems, excites you, then this is your fit.

Job Responsibilities:

• Own end-to-end infrastructure right from non-prod to prod environment including self-managed DBs
• Codify our infrastructure
• Do what it takes to keep the uptime above 99.99%
• Understand the bigger picture and sail through the ambiguities
• Scale technology considering cost and observability and manage end-to-end processes
• Understand DevOps philosophy and evangelize the principles across the organization
• Strong communication and collaboration skills to break down the silos

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!

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

DevOps Consultant!! MERN Stack Project Manager – Systems (Enterprise or Solutions) Architect needed!

Hello superstar,

I appreciate you taking time to read this. I have posted a job for developers to work on a start-up, the link is ......

I would need someone with DevOps experience, to ensure that the project is undertaken with the highest standards possible. I have had many experiences where ‘completed’ software after years of development was filled with bugs and it would be more cost-effective to start from scratch than to attempt to find and correct all the bugs.

I have attempted to learn as much as possible, but I now have an opportunity and it would better serve the venture to have someone handle the management of the project to ensure that;

• We choose the most appropriate technology
• We choose competent developers in those technologies
• The architecture and data modeling are clearly defined in a ‘blueprint’ plan
• A DevOps environment and processes are set up and the developers understand what is required
• Proper tests are carried out to ensure everything works as intended
• There are processes for testers to follow and competent testers are selected to follow them
• Accessibility, localization, and internationalization are planned ahead of time
• Security, scalability, and other future probabilities that I may not even be aware of are considered and planned ahead of time
• Documentation and code reviews, refactoring and other quality assurance processes are undertaken
• Working software is produced and systems that enable new developers or teams of people to easily take over and/or contribute new modules or updates in a controlled and organized fashion
• Cost estimates or budgets/projections or use of SaaS, hosting and other 3rd party services and applications

I am more concerned with a professional and world-class organizational system than with any particular type of software been produced as the strong foundation will enable anything to be creating with efficacy and precision.

Again, thank you for reading this, please reply with the word “superstar” anywhere in the second line of your response.  I look forward to hearing from you.

Warm wishes DevOps Evangelist,