As a Lead Platform Engineer you will design and build cloud-based distributed systems that solve complex business challenges for some of the world s largest companies
You will draw on your deep software engineering, cloud engineering, and DevOps expertise to design and build technology stacks and platform components that enable cross functional AI Engineering teams to create robust, observable and scalable solutions
As a member of a diverse and globally distributed engineering team, you will participate in the full engineering life cycle which includes designing, developing, optimizing, and deploying solutions and infrastructure at the scale of the world s largest companies
Core Responsibilities:
Cloud solution and distributed systems architecture for full stack AI software and data solutions
Implementation, testing and management of Infrastructure as Code (IAC) of cloud-based solutions that may include CI/CD, data integrations, APIs, web and mobile apps, and AI solutions
Defining and implementing scalable, observable, manageable, and self-healing cloud-based solutions across AWS, Google Cloud and Azure
Collaborate with cross-functional teams, including product managers, data scientists, and other engineers, to define and implement analytics and AI features and functionality that meet business requirements and user needs.
Utilize Kubernetes and containerization technologies to deploy, manage, and scale analytics applications in cloud environments, ensuring optimal performance and availability.
Develop and maintain APIs and microservices to expose analytics functionality to internal and external consumers, adhering to best practices for API design and documentation.
Implement robust security measures to protect sensitive data and ensure compliance with data privacy regulations and organizational policies.
Continuously monitor and troubleshoot application performance, identifying and resolving issues that impact system reliability, latency, and user experience.
Participate in code reviews and contribute to the establishment and enforcement of coding standards and best practices to ensure high-quality, maintainable code.
Stay current with emerging trends and technologies in cloud computing, data analytics, and software engineering, and proactively identify opportunities to enhance the capabilities of the analytics platform.
Collaborate closely with and influence business consulting staff and leaders as part of multi-disciplinary teams to assess opportunities and develop analytics solutions for Bain clients across a variety of sectors.
Influence, educate and directly support the analytics application engineering capabilities of our clients
ABOUT YOU
masters degree in Computer Science, Engineering, or a related technical field.
6+ years experience and atleast 3+ years at Staff level or equivalent
Proven experience as a cloud engineer and software engineer within either/or product engineering or professional services organisations
Experience designing and delivering cloud-based distributed solutions. GCP, AWS, or Azure certifications are a benefit
Experience building infrastructure as code with tools such as Terraform (preferred), Cloud Formation, Pulumi, AWS CDK, CDKTF, etc
Deep familiarity with nuances of software development lifecycle
One or more configuration management tools: Ansible, Salt, Puppet, or Chef
One or more monitoring and analytics platforms: Grafana, Prometheus, Splunk, SumoLogic, NewRelic, DataDog, CloudWatch, Nagios/Icinga
Experience building backend APIs, services and/or integrations with Python
Practitioner experience with Kubernetes through services like GKE, EKS or AKS is a benefit
Ability to work closely with internal and client teams and stakeholders
Use Git as your main tool for versioning and collaborating
Exposure to LLMs, Prompt engineering, Langchain a plus
Experience with workflow orchestration - doesn t matter if it s dbt, Beam, Airflow, Luigy, Metaflow, Kubeflow, or any other
Experience implementation of large-scale structured or unstructured databases, orchestration and container technologies such as Docker or Kubernetes
Strong interpersonal and communication skills, including the ability to explain and discuss complex engineering technicalities with colleagues and clients from other disciplines at their level of cognition
Curiosity, proactivity and critical thinking
Strong computer science fundaments in data structures, algorithms, automated testing, object-oriented programming, performance complexity, and implications of computer architecture on software performance.
Strong knowledge in designing API interfaces
Knowledge of data architecture, database schema design and database scalability
