Product Manager

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.

1. Development of new web applications.
2. Maintenance of existing web applications.
3. Writing server-side web application logic in Ruby.
4. Create back-end components.
5. Connect the application with the other web services.
6. Extending and maintaining JSON and Liquid-based APIs.

Job Purpose and Impact:

The Sr. Generative AI Engineer will architect, design and develop new and existing GenAI solutions for the organization. As a Generative AI Engineer, you will be responsible for developing and implementing products using cutting-edge generative AI and RAG to solve complex problems and drive innovation across our organization. You will work closely with data scientists, software engineers, and product managers to design, build, and deploy AI-powered solutions that enhance our products and services in Cargill. You will bring order to ambiguous scenarios and apply in depth and broad knowledge of architectural, engineering and security practices to ensure your solutions are scalable, resilient and robust and will share knowledge on modern practices and technologies to the shared engineering community.

Key Accountabilities:

• Apply software and AI engineering patterns and principles to design, develop, test, integrate, maintain and troubleshoot complex and varied Generative AI software solutions and incorporate security practices in newly developed and maintained applications.

• Collaborate with cross-functional teams to define AI project requirements and objectives, ensuring alignment with overall business goals.

• Conduct research to stay up-to-date with the latest advancements in generative AI, machine learning, and deep learning techniques and identify opportunities to integrate them into our products and services, optimizing existing generative AI models and RAG for improved performance, scalability, and efficiency, developing and maintaining pipelines and RAG solutions including data preprocessing, prompt engineering, benchmarking and fine-tuning.

• Develop clear and concise documentation, including technical specifications, user guides and presentations, to communicate complex AI concepts to both technical and non-technical stakeholders.

• Participate in the engineering community by maintaining and sharing relevant technical approaches and modern skills in AI.

• Contribute to the establishment of best practices and standards for generative AI development within the organization.

• Independently handle complex issues with minimal supervision, while escalating only the most complex issues to appropriate staff.

Minimum Qualifications:

• Bachelor’s degree in a related field or equivalent experience

• Minimum of five years of related work experience

• You are proficient in Python and have experience with machine learning libraries and frameworks

• Have deep understanding of industry leading Foundation Model capabilities and its application.

• You are familiar with cloud-based Generative AI platforms and services

• Full stack software engineering experience to build products using Foundation Models

• Confirmed experience architecting applications, databases, services or integrations.

we are looking for a resilient, emphatic Business Development Executive to contribute to the company's growth.

Designer interactive: do you think in UI/UX and can iterate ideas quickly? Do you love design and design things all the time regardless of the medium? Must show a portfolio demonstrating typography, design,  color usage and show you understand UI/UX. We are looking for full-time and freelance talent.

Must know:

Figma

Should know:

Illustrator

Photoshop

Nice to know a tool(s) for quick iteration:

Coding?

Web flow?
Something else?

Must haves:

Must speak English, must have portfolio online

Roles & Responsibilities:

• Write well-designed, testable, efficient, and high-quality code.
• Design (LLD & HLD) and architect technical solutions for the business problems of a large-scale system.
• Prepare and produce releases of software components.
• Support continuous improvement by investigating alternatives and technologies.
• Contribute to all phases of the development lifecycle.
• Lead the backend engineering team with examples.

Requirements:

• 5+ years’ experience under your belt developing performance-critical applications that run in a production environment using Node.Js
• Expertise in Database modeling, preferably relational DB, spotting DB bottlenecks, and writing optimized DB queries.
• Expertise in common Cloud technologies, AWS/GCP Knowledge of CI/CD tools and workflows.
• Knowledge in one or more of the following: NoSQL (especially, but not only Redis), Queueing system experience (Kafka, RabbitMQ, ActiveMQ, MSMQ)
• Good stakeholder management skills, with an ability to build trust and rapport with internal and external stakeholders
• It is good to have knowledge of Kubernetes and Docker and related deployment technologies.