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Engineering Lead- Backend, DevOps & Delivery
Technology-first B2B Commerce Startup
Engineering Lead- Backend, DevOps & Delivery

Engineering Lead- Backend, DevOps & Delivery at Technology-first B2B Commerce Startup · Bengaluru (Bangalore) · 4 - 8 years · Upto ₹25L / yr · Posted 7 Oct 2026

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Engineering Lead- Backend, DevOps & Delivery

at Technology-first B2B Commerce Startup

Agency job
4 - 8 yrs
Upto ₹25L / yr (Varies
)
Bengaluru (Bangalore)
Skills
skill iconPython
FastAPI
Django REST Framework
skill iconPostgreSQL
SQLAlchemy
REST API design
JWT authentication
skill iconDocker
skill iconAmazon Web Services (AWS)
skill iconGit
skill iconRedis
skill iconElastic Search

We are looking for a hands-on Engineering Lead who can own backend engineering, DevOps, and delivery management.

This is not a coordination-only role. You will contribute technically while establishing the processes, accountability, and

operating rhythm needed for predictable delivery. You will manage multiple engineers and effectively use AI coding

agents and automation tools to improve the team's output without compromising quality.


Key Responsibilities

• Own the architecture, development, deployment, and reliability of our backend systems.

• Lead engineers through clear goals, task breakdowns, ownership, reviews, and regular feedback.

• Establish a process-oriented delivery system covering planning, estimation, execution, testing, release, and

retrospectives.

• Translate product requirements into technical plans, milestones, dependencies, and measurable deliverables.

• Track execution proactively, identify delivery risks early, and communicate status clearly.

• Remain hands-on with critical development, debugging, architecture, code reviews, and production issues.

• Own CI/CD, infrastructure, observability, deployment practices, and operational readiness.

• Improve engineering quality through coding standards, automated testing, documentation, and review processes.

• Delegate work effectively across team members according to their strengths and development needs.

• Use AI coding agents and engineering automation to parallelise suitable work, while reviewing and validating all

outputs.

• Build a culture of accountability in which work has clear owners, acceptance criteria, deadlines, and definitions of

done.

• Collaborate closely with product and business stakeholders to balance speed, scope, quality, and technical debt.


What We Are Looking For

• 6+ years of backend engineering experience.

• At least 2 years of experience leading or managing an engineering team.

• Strong hands-on backend development experience with Python, FastAPI, Django + Django REST Framework, and

PostgreSQL.

• Practical experience with cloud infrastructure (AWS), DevOps, CI/CD, containers (Docker), monitoring, and

production operations.

• Experience working in a mature, process-oriented engineering environment.

• Demonstrated ability to improve delivery predictability and engineering throughput.

• Strong understanding of agile planning, estimation, sprint execution, incident management, and retrospectives.

• Ability to manage multiple people and concurrent streams of work.

• Experience reviewing architecture, pull requests, test plans, and production-readiness requirements.

• Strong written and verbal communication skills.

• Ability to give direct, constructive feedback and hold team members accountable.

• Sound judgement about when to build, delegate, automate, or reduce scope.


Tech Stack

Must-have: Python, FastAPI, Django + Django REST Framework, PostgreSQL, SQLAlchemy (async) + asyncpg, Pydantic,

REST API design + JWT authentication, Docker, AWS (ECS, ALB, ECR, CloudWatch, S3, Secrets Manager), Git.


Strongly preferred: Redis, Elasticsearch / OpenSearch, WebSockets, Payment gateways (Razorpay / Cashfree), Third-

party / ERP integrations, CI/CD (AWS CodeBuild or GitHub Actions).


Nice-to-have: Frappe / ERPNext + MariaDB, Microservices architecture, PostHog / CleverTap / Firebase, B2B e-commerce

or order management experience.


AI-Assisted Engineering

The ideal candidate should be comfortable incorporating AI into the software-development lifecycle. This includes:

• Breaking work into tasks that can be delegated safely to coding agents.

• Running multiple human and agent-led workstreams concurrently.

• Providing agents with clear context, constraints, and acceptance criteria.

• Reviewing generated code for correctness, security, maintainability, and architectural fit.

• Using automation to accelerate testing, documentation, refactoring, and operational workflows.

• Measuring whether AI tooling produces genuine delivery improvements.

We do not expect blind reliance on generated output. The Engineering Lead remains accountable for the quality and

correctness of everything shipped by the team.

Success in the First 90 Days

• Understand the existing architecture, infrastructure, team capabilities, and delivery bottlenecks.

• Take ownership of backend and DevOps execution.

• Introduce a consistent planning, tracking, review, and release cadence.

• Establish visibility into ownership, progress, dependencies, and delivery risks.

• Improve CI/CD, testing, monitoring, and production-readiness practices.

• Create an effective delegation model across engineers and AI agents.

• Deliver at least one meaningful product or platform milestone predictably from planning through production.


Nice to Have

• Experience scaling an early-stage or growing engineering team.

• Experience hiring, mentoring, and performance-managing engineers.

• Experience with infrastructure as code, security practices, and cloud-cost optimisation.

• Experience managing migrations, reliability improvements, or legacy-system modernisation.

• Previous experience introducing AI-assisted development workflows into an engineering team.


Who Will Succeed in This Role

You are likely to succeed if you are equally comfortable discussing architecture, reviewing code, resolving a production incident, planning a release, and having an accountability conversation with an engineer. You bring structure without creating unnecessary bureaucracy and know how to turn product priorities into consistent, high-quality delivery.

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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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Job Description:


Head of Engineering — Chennai (Onsite)About the Role


Job Details


Position: Head of Engineering

Department: Engineering

Experience: 6–8 Years

Employment Type: Full-time

Location: Chennai, On-site

Salary: Up to ₹24,00,000 per annum


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How to Apply




Join Aura Gold and be part of building a high-performing team from the ground up!


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  • Provide regular updates on progress, challenges, and innovations to stakeholders.

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Skills & Qualifications:

Must-Have Skills:

  • Proven experience as an Engineering Manager or similar leadership role.
  • Strong technical background in backend technologies (e.g., Java, Spring Framework).
  • Deep understanding of RESTful API design, microservices architecture.
  • Hands-on experience with containerization and orchestration tools like Docker.
  • Solid knowledge of Agile methodologies, TDD, and CI/CD pipelines.
  • Excellent leadership, communication, and problem-solving skills.
  • Expertise in integrating Large Language Models (LLMs) and AI-powered tools into development workflows.


Nice-to-Have Skills:

  • Experience with cloud platforms like AWS, GCP, or Azure.
  • Knowledge of automated testing frameworks for both backend (e.g., JUnit, Postman) and frontend (e.g., Playwright).
  • Understanding of DevOps practices and infrastructure-as-code tools like Terraform.
  • Exposure to AI/ML frameworks and libraries for enhancing application features and team efficiency.
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Amura Health
at Amura Health
3 candid answers
1 video
Swathi S
Posted by Swathi S
Chennai
7 - 12 yrs
₹30L - ₹55L / yr
skill iconAmazon Web Services (AWS)
skill iconPython
CI/CD
DevOps
Platform as a Service (PaaS)
+7 more

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)


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Remote only
6 - 12 yrs
₹45L - ₹50L / yr
skill iconPython
skill iconReact.js
skill iconJavascript
API management
RESTful APIs

About the Role

We are seeking a hands-on Tech Lead to design, build, and integrate AI-driven systems that automate and enhance real-world business workflows. This is a high-impact role for someone who enjoys full-stack ownership — from backend AI architecture to frontend user experiences — and can align engineering decisions with measurable product outcomes.

You will begin as a strong individual contributor, independently architecting and deploying AI-powered solutions. As the product portfolio scales, you will lead a distributed team across India and Australia, acting as a System Integrator to align engineering, data, and AI contributions into cohesive production systems.

Example Project

Design and deploy a multi-agent AI system to automate critical stages of a company’s sales cycle, including:

  • Generating client proposals using historical SharePoint data and CRM insights
  • Summarizing meeting transcripts
  • Drafting follow-up communications
  • Feeding structured insights into dashboards and workflow tools

The solution will combine RAG pipelines, LLM reasoning, and React-based interfaces to deliver measurable productivity gains.

Key Responsibilities

  • Architect and implement AI workflows using LLMs, vector databases, and automation frameworks
  • Act as a System Integrator, coordinating deliverables across distributed engineering and AI teams
  • Develop frontend interfaces using React/JavaScript to enable seamless human-AI collaboration
  • Design APIs and microservices integrating AI systems with enterprise platforms (SharePoint, Teams, Databricks, Azure)
  • Drive architecture decisions balancing scalability, performance, and security
  • Collaborate with product managers, clients, and data teams to translate business use cases into production-ready systems
  • Mentor junior engineers and evolve into a broader leadership role as the team grows

Ideal Candidate Profile

Experience Requirements

  • 5+ years in full-stack development (Python backend + React/JavaScript frontend)
  • Strong experience in API and microservice integration
  • 2+ years leading technical teams and coordinating distributed engineering efforts
  • 1+ year of hands-on AI project experience (LLMs, Transformers, LangChain, OpenAI/Azure AI frameworks)
  • Prior experience in B2B SaaS environments, particularly in AI, automation, or enterprise productivity solutions

Technical Expertise

  • Designing and implementing AI workflows including RAG pipelines, vector databases, and prompt orchestration
  • Ensuring backend and AI systems are scalable, reliable, observable, and secure
  • Familiarity with enterprise integrations (SharePoint, Teams, Databricks, Azure)
  • Experience building production-grade AI systems within enterprise SaaS ecosystems




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