DevOps Engineer

Job Title : DevOps Engineer – Fintech (Product-Based)

Experience : 5+ Years

Location : Mumbai

Job Type : Full-Time | Product Company

Role Summary :

We are hiring a DevOps Engineer with strong product-based experience to manage infrastructure for a Fintech platform built on stateful microservices.

The role involves working across hybrid cloud + on-prem, with deep expertise in Kubernetes, Helm, GitOps, IaC, and Cloud Networking.

Mandatory Skills :

Product-based experience, deep Kubernetes (managed & self-managed), custom Helm Chart development, ArgoCD/FluxCD (GitOps), strong AWS/Azure cloud networking & security, IaC module development (Terraform/Pulumi/CloudFormation), experience with stateful microservices (DBs/queues/caches), multi-tenant deployments, HA/load balancing/SSL/TLS/cert management.

Key Responsibilities :

• Deploy and manage stateful microservices in production.
• Handle both managed & self-managed Kubernetes clusters.
• Develop and maintain custom Helm Charts.
• Implement GitOps pipelines using ArgoCD/FluxCD.
• Architect and operate secure infra on AWS/Azure (VPC, IAM, networking).
• Build reusable IaC modules using Terraform/CloudFormation/Pulumi.
• Design multi-tenant cluster deployments.
• Manage HA, load balancers, certificates, DNS, and networking.

Mandatory Skills :

• Product-based company experience.
• Strong Kubernetes (EKS/AKS/GKE + self-managed).
• Custom Helm Chart development.
• GitOps tools : ArgoCD/FluxCD.
• AWS/Azure cloud networking & security.
• IaC module development (Terraform/Pulumi/CloudFormation).
• Experience with stateful components (DBs, queues, caches).
• Understanding of multi-tenant deployments, HA, SSL/TLS, ingress, LB.

Job Title : Azure DevOps Engineer

Experience Required : 7+ Years

Work Mode : Remote / Hybrid

Location : Remote

Notice Period : Immediate Joiners / Serving Candidates (within 20 days only)

Interview Mode : Face-to-Face or Virtual

Open Positions : 2

Job Description :

We are seeking an experienced Azure DevOps Engineer with 7+ years of relevant experience in DevOps practices, especially around Azure infrastructure, deployment automation, and CI/CD pipeline management. The ideal candidate should have hands-on expertise with Azure DevOps, GitHub, YAML, and Azure services, along with solid communication and coordination capabilities.

Mandatory Skills : Azure DevOps, GitHub Actions, YAML, Bicep, Azure services (App Gateway, WAF, NSG, CosmosDB, Storage Accounts), Unix scripting, and Azure Fundamentals certification.

Key Responsibilities :

• Manage deployments for Dynamics 365 and proxy applications
• Run and maintain ADO pipelines and GitHub Actions
• Ensure proper status updates on ADO Boards and deployed work items
• Coordinate with QA teams to execute smoke testing post-deployment
• Communicate deployment progress across team channels effectively
• Monitor deployment cycles, approval gates, logs, and alerts
• Ensure smooth integration of infrastructure and DevOps practices

Mandatory Skills :

• Minimum 7+ years in DevOps, with strong experience in Azure DevOps (ADO).
• Proven expertise in building pipelines using Azure DevOps and GitHub.
• Proficiency in Bicep, YAML scripting, and Azure Infrastructure-as-Code (IaC).
• Hands-on with Azure services like :
• App Gateway, WAF, NSG, CosmosDB, Storage Accounts.
• vNet, Managed Identity, KeyVault, AppConfig, App Insights.
• Basic Azure Fundamentals Certification (AZ-900).
• Excellent communication skills in English.

Nice to Have :

• Experience in managing large enterprise-scale deployments.
• Familiarity with branching strategies and monitoring tools.
• Exposure to Approval Gates and Deployment Governance.

Senior MLOps Engineer

LLM Operations, Observability & Eval Infrastructure

📍 Mumbai (On-site) | Full-time | 5-7 years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

We are hiring a Senior MLOps Engineer for a dedicated client engagement focused on building an AI-powered application builder platform. The platform consumes LLMs at scale through provider APIs.

This role owns the operational discipline around production LLM consumption - increasingly called LLMOps - covering observability, evaluation infrastructure, model lifecycle, cost operations, prompt deployment, and agent run reliability.

The mandatory requirement is hands-on production experience operating LLM-backed systems, with a strong DevOps or SRE foundation. This is not a model training or ML science role.

The work is making the system around the AI engineer's designs observable, controlled, reliable, and economically accountable. You will pair daily with the Senior AI Engineer, who designs prompts, evals, and agent behaviour - you operationalise those systems for production.

A typical week includes a tracing audit on a degraded agent run, an eval pipeline build for a new model release, a cost attribution review, and a staged prompt rollout.

Responsibilities:

Observability and Tracing

Build and own end-to-end tracing for agent runs: every prompt, response, tool call, token count, latency, and cost, linked to user session and project.

Stand up and operate LLM observability tooling (Langfuse, LangSmith, Braintrust, or Arize Phoenix).

Make debugging a single bad agent run among thousands a routine workflow through searchable traces, failure taxonomies, and dashboards segmented by task type.

Evaluation Infrastructure as a Production System

Operationalise the eval suite designed by the Senior AI Engineer: automated execution in CI on every prompt or model change, with results stored and trended over time.

Implement regression gates that block quality-degrading changes from shipping.

Build production sampling to continuously score a sample of real agent runs and catch quality drift that offline evals miss.

Model Lifecycle Management

Pin model versions, never "latest".

Own the upgrade process: run the eval suite against new model releases and manage eval-gated migrations.

Maintain fallback chains across providers for graceful degradation or queueing during outages.

Track provider deprecation schedules and plan migrations ahead of forced cutoffs.

Cost Operations

Implement per-user and per-task cost attribution - token spend is the platform's largest variable cost and requires the same rigour as cloud cost management.

Set up budget alerts and anomaly detection so a single user or bug cannot burn significant spend overnight.

Monitor prompt cache hit rates and quantify savings.

Manage capacity planning around provider rate limits, including quota negotiation and throughput tiering.

Prompt and Configuration Deployment

Treat prompts as production artifacts: version control for prompts and agent configurations, staged rollout infrastructure (deploy a prompt change to a percentage of traffic before full rollout), A/B testing infrastructure, instant rollback, and audit history covering which prompt version served which user and when.

Reliability Engineering for Agent Runs

Agent runs are long, stateful, and failure-prone.

Own retry and resume semantics so a run that fails mid-way does not restart from scratch.

Implement timeouts and circuit breakers on provider calls, dead-letter handling for failed runs, and queue and concurrency management for agent workloads.

SLO Ownership and Incident Response

Define and track SLOs for agent run latency and completion rates.

Lead incident response when SLOs are breached.

Write postmortems.

Surface reliability risks proactively before they reach users.

Safety and Compliance Operations

Run the moderation pipeline (prompt and output classification) in production.

Monitor for abuse patterns and own incident response when the agent misbehaves at scale.

Maintain audit logs and implement data retention and residency policies for prompts and generated code as enterprise requirements emerge.

AI-Assisted Engineering Discipline

Use Claude, Cursor, and similar tools day to day for infrastructure code, scripts, and pipelines.

Set the team standard for safe use, review, and validation of AI-generated infrastructure before it ships.

Requirements:

Hands-on production ownership of LLM-backed systems in operation (mandatory).

Must have personally shipped and operated at least one LLM-powered system in production, with operational responsibility including oncall, incident response, and reliability ownership.

Alternatively: strong DevOps or SRE background with demonstrated hands-on familiarity with LLMOps tooling (Langfuse, LangSmith, Braintrust, Arize, or equivalent).

POCs and lab work do not qualify.

5+ years of overall engineering experience

With at least 2 years in DevOps, SRE, platform engineering, or LLM operations roles.

This is not an ML science role.

A DevOps or SRE background with a substantive pivot into LLMOps is a strong qualification.

Observability and Tracing Depth

Production experience with LLM observability tooling - Langfuse, LangSmith, Braintrust, or Arize Phoenix.

Comfortable instrumenting with OpenTelemetry, Prometheus, and Grafana.

Able to build and search trace pipelines, define failure taxonomies, and surface quality signals from production traffic.

CI/CD and Quality Gate Experience

Strong with GitHub Actions or GitLab CI.

Experience building automated quality gates: eval-gated pipelines, regression enforcement, or coverage gates that block degrading changes from shipping.

Cost Management and Attribution for Usage-Based Services

Experience owning cost attribution for cloud API spend or equivalent.

Comfortable with budget alerts, anomaly detection, and per-user or per-task cost breakdowns.

Reliability Engineering for Long-Running, Stateful Workloads

Experience with queues, retry patterns, idempotency, and failure recovery on asynchronous or multi-step workloads.

Comfortable defining SLOs and being accountable for them on production systems.

Multi-Provider API Management

Familiarity with LLM provider rate limits, version pinning, fallback chains, and quota management across OpenAI, Anthropic, Google, or equivalent.

Infrastructure as Code and Deployment Automation

Hands-on with Terraform or Pulumi and Docker.

AWS working knowledge (EC2, S3, IAM, EKS or ECS).

Strong with CI/CD for deploying services and configuration changes safely.

Nice to Have

• Experience with prompt A/B testing or staged rollout infrastructure
• Workflow orchestration (BullMQ, Temporal, Celery)
• Content moderation pipeline experience
• Data residency and compliance requirements for AI systems
• Kubernetes (EKS) in production
• AWS certifications

We are looking for very hands-on DevOps engineers with 3 to 6 years of experience. The person will be part of a team that is responsible for designing & implementing automation from scratch for medium to large scale cloud infrastructure and providing 24x7 services to our North American / European customers. This also includes ensuring ~100% uptime for almost 50+ internal sites. The person is expected to deliver with both high speed and high quality as well as work for 40 hours per week (~6.5 hours per day, 6 days per week) in shifts that will rotate every month.

This person MUST have:

• B.E Computer Science or equivalent

• 2+ Years of hands-on experience troubleshooting/setting up of the Linux environment, who can write shell scripts for any given requirement.

• 1+ Years of hands-on experience setting up/configuring AWS or GCP services from SCRATCH and maintaining them.

• 1+ Years of hands-on experience setting up/configuring Kubernetes & EKS and ensuring high availability of container orchestration.

• 1+ Years of hands-on experience setting up CICD from SCRATCH in Jenkins & Gitlab.

• Experience configuring/maintaining one monitoring tool.

• Excellent verbal & written communication skills.

• Candidates with certifications - AWS, GCP, CKA, etc will be preferred

• Hands-on experience with databases (Cassandra, MongoDB, MySQL, RDS).

Experience:

• Min 3 years of experience as DevOps automation engineer buildingg, running, and maintaining production sites.

• Not looking for candidates who have experience only as L1/L2 or Build & Deploy.
  
  

Location: Remotely, anywhere in India.

Timings:

• The person is expected to deliver with both high speed and high quality as well as work for 40 hours per week (~6.5 hours per day, 6 days per week) in shifts that will rotate every month.
  
  

Position:

• Full time/Direct

• We have great benefits such as PF, medical insurance, 12 annual company holidays, 12 PTO leaves per year, annual increments, Diwali bonus, spot bonuses and other incentives etc.

• We dont believe in locking in people with large notice periods.  You will stay here because you love the company.  We have only a 15 days notice period.

We are having an excellent job opportunity for the position for AWS Infra Architect for one of the reputed Multinational Company at Hyderabad.

Mandate Skills : Please find the below expectations

• We need at-least 3+ years of experience as an Architect in AWS Primary Skills
• Designing, Planning, Implementation , Providing the solutions in Designing the Architecture
• Automation Using Terraform / Powershell /Python
• Should have good experience in Cloud formation Templates
• Experience in Cloudwatch
• Security in AWS
• Strong Linux Administration skills

As an Infrastructure Engineer at Navi, you will be building a resilient infrastructure platform, using modern Infrastructure engineering practices.

You will be responsible for the availability, scaling, security, performance and monitoring of the navi Cloud platform. You’ll be joining a team that follows best practices in infrastructure as code

• Build out the Infrastructure components like API Gateway, Service Mesh, Service Discovery, container orchestration platform like kubernetes.
• Developing reusable Infrastructure code and testing frameworks
• Build meaningful abstractions to hide the complexities of provisioning modern infrastructure components
• Design a scalable Centralized Logging and Metrics platform
• Drive solutions to reduce Mean Time To Recovery(MTTR), enable High Availability.

• Good to have experience in managing large scale cloud infrastructure, preferable AWS and Kubernetes
• Experience in developing applications using programming languages like Java, Python and Go
• Experience in handling logs and metrics at a high scale.
• Systematic problem-solving approach, coupled with strong communication skills and a sense of ownership and drive.

As DevOps Engineer, you are responsible to setup and maintain GIT repository, DevOps tools like Jenkins, UCD, Docker, Kubernetes, Jfrog Artifactory, Cloud monitoring tools, Cloud security.

• Setup, configure, and maintain GIT repos, Jenkins, UCD, etc. for multi hosting cloud environments.
• Architect and maintain the server infrastructure in AWS. Build highly resilient infrastructure following industry best practices.
• Working on Docker images and maintaining Kubernetes clusters.
• Develop and maintain the automation scripts using Ansible or other available tools.
• Maintain and monitor cloud Kubernetes Clusters and patching when necessary.
• Working on Cloud security tools to keep applications secured.
• Participate in software development lifecycle, specifically infra design, execution, and debugging required to achieve successful implementation of integrated solutions within the portfolio.
• Required Technical and Professional Expertise.
• Minimum 4-6 years of experience in IT industry.
• Expertise in implementing and managing Devops CI/CD pipeline.
• Experience in DevOps automation tools. And Very well versed with DevOps Frameworks, Agile.
• Working knowledge of scripting using shell, Python, Terraform, Ansible or puppet or chef.
• Experience and good understanding in any of Cloud like AWS, Azure, Google cloud.
• Knowledge of Docker and Kubernetes is required.
• Proficient in troubleshooting skills with proven abilities in resolving complex technical issues.
• Experience with working with ticketing tools.
• Middleware technologies knowledge or database knowledge is desirable.
• Experience and well versed with Jira tool is a plus.