CAW.Tech

The Role

We are looking for a Senior Product Manager to embed on-site with one of CAW's enterprise customers — sitting with their functional leaders, understanding their workflows, identifying the highest-ROI automation opportunities, and prototyping solutions yourself using AI-assisted development tools like Claude Code or OpenAI Codex.

This is not a project management role. We are looking for someone with strong business analysis chops who can walk into a domain they've never worked in, study it quickly, break down complex workflows, and figure out what to build. You should have a coding background and be able to prototype working solutions yourself — not just write specs and hand them off. You stay involved throughout production so nothing is lost in the communication between the customer and our remote engineers.

You are the one person the customer sees. The building and running happen behind you, at CAW HQ, on Gantry.

A Typical Week

Monday–Tuesday: Interview functional leaders and their team members to gain a deep understanding of their current processes, pain points, and undocumented workflows. Map what you find.

Wednesday: Apply structured prioritization frameworks (RICE, ICE, or equivalent) to rank discovered use cases by ROI. Classify each problem as deterministic automation, AI automation, an AI agent (built on Gantry), or a Custom Ops Platform (COP) opportunity — and draft the solution approach in collaboration with CAW's engineering and AI architects at HQ.

Thursday: Present the top problems and proposed solutions to the functional leader for buy-in. Get alignment on what to tackle first.

Friday and the following week: Prototype the solution using Claude Code, Codex, or equivalent AI-assisted development tools. Target: a working prototype in under a week. Present the prototype to the functional team, collect feedback, and iterate until it meets their needs. Once validated, draft a proposal to productionize it on Gantry (or as a COP) in collaboration with HQ, and drive approval from the functional head.

Ongoing: Spend a few hours daily collaborating with CAW's engineering teams at HQ to keep productionizing on track — while simultaneously starting discovery on the next problem with the next functional leader.

Key Responsibilities

Use Case Mining & Discovery

• Embed on-site with the customer. Sit with functional leaders across departments — operations, finance, CX, supply chain, HR — to understand their workflows, pain points, and priorities.
• Don't wait for requirements to be handed to you. Proactively identify problems CAW can solve with agents on Gantry or with a Custom Ops Platform. Prioritize ruthlessly based on business impact, technical feasibility, and speed to value.
• Run structured discovery sessions. Map workflows. Document undocumented processes. Find the use cases the customer doesn't even know they have.
• Build a use-case pipeline: a ranked, living backlog of agent and COP opportunities for you and the customer's leadership to review regularly. This backlog is how the account grows.

Solution Design & Rapid Prototyping

• Classify each problem as deterministic automation, AI automation, an AI agent, or a COP. Draft solution approaches in collaboration with CAW HQ architects.
• Prototype solutions yourself using Claude Code, Codex, or equivalent AI-assisted development tools. A working demo in under a week is the standard.
• Translate validated prototypes into structured specs — clear problem statements, acceptance criteria, scope boundaries, and success metrics — that CAW's engineers can productionize on Gantry without ambiguity.
• Work in tight weekly cycles: discover, prototype, validate, spec, hand off for production — and stay involved through delivery.

Customer Relationship & Adoption

• You represent CAW inside the customer's organization. Build deep trust with functional leaders and CXOs.
• Own delivery timelines, customer expectations, and escalations. You are the single point of accountability the customer sees.
• Ensure what gets built is what gets used. Drive adoption by working with the customer's end users, running training sessions, and instrumenting usage metrics.
• Know when to push (the customer is avoiding a hard decision) and when to listen (there's a real constraint you haven't understood yet).

Platform & Product Feedback Loop

• Surface structured insights back to the Gantry product team: capability gaps, integration friction, customer objections, and use case patterns that should become reusable agents or platform features.
• Spot which agents and patterns are reusable across customers so they can be added to CAW's library and accelerate the next engagement. Flag when a customer ask is a one-off vs. when it's a pattern many customers share.
• Deliver actionable, prioritized field intelligence — not a dump of Slack messages. Your feedback directly informs the Gantry roadmap.

What We're Looking For

• 4–7 years of experience in a customer-facing product or business analysis role, such as Product Manager, Business Analyst, Solutions Consultant, or Forward Deployed Engineer at a SaaS/AI company.
• Coding background - You have written code professionally or as part of product work. You understand APIs, data flows, and system architecture well enough to prototype, not just spec.
• Demonstrable AI-assisted development experience. Show us products,  prototypes, or internal tools you have built using Claude Code, OpenAI Codex, Cursor, or equivalent tools. We want to see output, not claims.
• Heavy business analysis experience. You have demonstrable examples of walking into a domain with vague or undefined requirements, studying it deeply, and cracking the problem. We are not looking for project managers who track tickets — we want people who figure out what the tickets should be.
• Strong speccing discipline. You can write a clear, buildable spec with acceptance criteria that an engineer can work from without ambiguity.
• Client-facing experience. Proven ability to run discovery with enterprise customers, manage stakeholders with competing priorities, and build trust with functional leaders and CXOs.
• Comfort with ambiguity. You will walk into customer organizations with no playbook. You need to figure out what matters, fast.
• Excellent communication. You can translate between a non-technical business leader and an engineer in the same meeting.
• Willingness to be on-site. This is an embedded role, not a remote one.
• Experience with AI/ML platforms, agent frameworks, automation tools, or enterprise SaaS is strongly preferred.
• A bias toward doing, not analyzing. You are measured by use cases shipped to production and prototypes that land, not decks presented.

Role:

We're scaling an AI platform that powers Computer Vision and real-time video analytics, and we need someone to own the architecture, not just contribute to it. Our AI workloads are moving to the cloud, our models need hardening for production, and our engineering practices need a north star. The gap between prototype and production is costing us velocity.

Responsibilities:

• Architect and govern the end-to-end AI platform from data lake to model serving on AWS (SageMaker preferred).
• Lead cloud migration of AI workloads with a cloud-native, containerised approach (Docker, Kubernetes, CI/CD).
• Own the AI roadmap model accuracy, MLOps maturity, observability, and scalability.
• Set engineering standards across ML development, deployment, and monitoring.
• Mentor engineers and data scientists; reduce key-person dependency across the org.
• Drive productionisation, turn research into reliable, monitored, high-performance systems.

Requirements:

• Deep expertise in Computer Vision, Deep Learning, and Video/Real-Time Analytics.
• Fluency in PyTorch and/or TensorFlow, Python, and ML architecture patterns.
• Hands-on with AWS SageMaker, Docker, Kubernetes, CI/CD pipelines.
• Experience with model monitoring, observability tools, and data lake architectures.
• A track record of leading AI strategy, not just executing it.

About the Role

We're looking for a hands-on AI Architect to lead the design and delivery of AI-native applications. You will define the AI stack, establish engineering standards, and drive adoption of AI-assisted development across teams.

Responsibilities

• Architect and build production-grade AI applications.
• Evaluate and standardize AI frameworks, tools, and platforms.
• Design agentic systems, RAG pipelines, and AI workflows.
• Establish AI evaluation, testing, guardrails, and observability practices.
• Drive AI-native development and delivery acceleration initiatives.
• Partner with engineering and product teams on technical roadmap decisions.

Requirements

• 7+ years of software engineering experience.
• Strong experience with LLMs, RAG, embeddings, prompt engineering, and agent frameworks.
• Experience with LangGraph, AutoGen, MCP, or similar ecosystems.
• Hands-on experience with AI evaluations (DeepEval, Ragas), guardrails, and structured outputs.
• Strong Python and TypeScript/Java skills.
• Experience with Kubernetes, CI/CD, IaC, observability, and event-driven architectures.
• Proven experience shipping AI applications to production.

We are seeking a skilled Senior QA Engineer with (2–5 years) experience and a strong foundation in backend API testing, AI system evaluation, and production-quality test automation. The ideal candidate will have at least 2 years of hands-on backend/API testing, strong coding skills in Python, TypeScript, or Java, with a passion for building eval infrastructure for AI systems.

Key Responsibilities:

• Design, develop, and execute eval datasets and regression harnesses for production AI systems - voice agents and enterprise chat platforms.
• Collaborate with AI engineering teams to embed quality gates into PR workflows - eval scores before merge, not after.
• Build and own LLM-as-judge harnesses, golden datasets, and prompt regression suites.
• Write and maintain automated test frameworks using Pytest, REST Assured, or equivalent coded frameworks.
• Perform API and backend testing across microservices and async LLM pipelines.
• Design observability dashboards so anyone can answer "did the AI get worse this week?" with a chart, not gut feel.
• Partner with engineering on red-teaming - adversarial datasets covering PII, jailbreaks, and prompt injection.
• Continuously research and recommend new eval tooling and testing strategies to improve AI system quality.

Key Requirements:

• 4–7 years of experience in QA / SDET / Quality Engineering.
• At least 1.5–2 years in backend / API / systems testing.
• 2+ years of strong coding in Python, TypeScript, or Java.
• 2+ years with modern test frameworks - Pytest / REST Assured / JUnit / Vitest / Jest.
• Hands-on with microservices, async pipelines, and event-driven architecture.
• Experience with CI/CD integration and test infrastructure.
• Builds automation frameworks from scratch - not just uses tools.
• Exposure to AI/LLM eval tooling: Langfuse, LangSmith, RAGAS, DeepEval, or equivalent (preferred).

Preferred Qualifications:

• Strong systems thinking - reasons about contracts, retries, latency, and failure modes, not just UI surfaces.
• Experience with observability tooling - OpenTelemetry, Datadog, or Honeycomb.
• Familiarity with voice/telephony testing, ASR/TTS evaluation, or regulated-domain QA (PII, audit trails, compliance).
• Excellent communication and collaboration skills.
• Ability to work independently and take full ownership of quality engineering.

As the ladder goes up, the expectations rise too, providing more responsibility and opportunities for growth.

Why Join Us:

• Greenfield eval infrastructure - build quality systems for production AI, not maintain legacy test suites.
• Real stakes: regulated industries, real customers, real money flows. Hallucinations are not allowed.
• Embedded in design from day one - eval scores in PR descriptions before merge, not a downstream gate.
• Work alongside modern AI coding tools (Claude Code, Codex) as part of normal development.
• Collaborative team with a strong emphasis on engineering rigor and continuous improvement.

About the Role

We are looking for a Cloud Product Owner who will drive the success of the InMotion Cloud platform by translating product strategy into clear, actionable direction for engineering teams. You will own the complete product lifecyclefrom market analysis and roadmap definition to launch and adoption optimization. This role demands a rare blend of technical depth, product thinking, and cross-functional leadership.

You will act as the voice of the customer in all product decisions, partner with engineering to deliver core cloud capabilities, and ensure alignment between technical architecture and business goals.

Responsibilities

• Product Strategy & Vision
• Be the functional and technical expert for how the product works, including underlying features that enable critical user journeys
• Research and analyze current product features and competitive landscape to define new capabilities that enhance sales and profitability
• Translate product strategy into detailed requirements, user stories, and prioritized backlogs
• Identify key pain points in cloud adoption (pricing complexity, performance, support, migration) and incorporate into roadmap
• Customer & Market Insights
• Gather and synthesize feedback from internal and external sources: customers, support, sales, and marketing
• Understand and evaluate current user experience, product usage patterns, and workflows
• Provide recommendations to improve experience and increase adoption
• Act as the primary liaison between engineering, sales, marketing, and support
• Execution & Delivery
• Work directly with engineering teams to deliver core cloud capabilities (compute, storage, networking, orchestration)
• Ensure alignment between technical architecture and customer-facing features
• Drive Agile product development processes: sprint planning, backlog refinement, release management, go-to-market (GTM)
• Analyze and report on team deliverables, milestones, impediments, and successes
• Support product launches, including internal enablement and external positioning
• Growth & Optimization
• Partner with marketing and sales to define messaging, packaging, and pricing strategies
• Track product adoption, usage metrics, and technical performance
• Continuously optimize the offering based on data and customer feedback
• Establish and maintain professional working relationships across all departments and sites

Requirements

• Experience & Knowledge:
• 3-5 years in an engineering leader, product management, or product ownership role, preferably in cloud, hosting, or infrastructure
• Strong understanding of cloud computing fundamentals (IaaS, virtualization, networking, storage)
• Experience working with technical teams in Agile/Scrum environments
• Ability to translate complex technical concepts into clear business value
• Strong analytical and problem-solving skills
• Experience preparing and presenting projections, estimates, and product/program performance reports
• A degree in software engineering, business, or a related field (or equivalent experience)

Skills & Abilities:

• Excellent written, verbal, and interpersonal communication skills
• Ability to communicate effectively across teams and departments
• Ability to work in a fast-paced, multi-faceted role, take initiative, and multi-task on complex projects
• Strong organizational skills with attention to detail
• Ability to meet deadlines and manage competing priorities

A Staff Engineer at CAW is a technical and people leader responsible for end-to-end project delivery and the growth of engineers. You own technical design (LLDs), contribute to HLD discussions, lead execution, ensure engineering best practices, and act as the primary technical point of contact for clients. You manage a set of engineers, supporting their career progression through mentorship, feedback, and performance development.

Responsibilities:

• Own Low-Level Design (LLD) and code quality; contribute to HLD discussions under guidance.
• Own end-to-end project delivery: requirements understanding, sprint planning, execution, risk management, and production readiness.
• Lead the project engineering team with clear technical direction, ownership, and execution accountability.
• Act as primary people manager for assigned engineers; own their career progression, mentorship, and growth.
• Provide regular 1:1s focused on skill development, feedback synthesis, and long-term career goals.
• Serve as primary technical point of contact for clients; build trust through clear communication and proactive problem-solving.
• Ensure best practices across architecture, implementation, testing, and production readiness.
• Establish disciplined AI usage across the team for design validation, reviews, testing, and documentation.

The core requirements for the job include the following:

Technical Expertise:

• In-depth knowledge of C#. NET languages and the DotNet Core Web API Framework.
• Strong SQL skills: complex queries, stored procedures, functions; PostgreSQL or SQL Server proficiency.
• Experience with Entity Framework or Dapper ORM.
• ReactJS/Angular frontend experience.
• Cloud, Agile, CI/CD, and DevOps environments.
• Solid grasp of SOLID Principles and OOPS Concepts.

Leadership and Ownership:

• Proven ability to own feature-level delivery end-to-end with minimal supervision.
• Strong people management skills with a track record of mentoring and developing engineers.
• Exposure to system design and architecture discussions.
• Production-first mindset with a focus on reliability, scalability, and operational excellence.

AI Systems and Engineering (Nice to Have):

• Advanced experience deploying RAG or agent-based systems with LangGraph orchestration.
• Expert-level mastery of async Python, system thinking, and building scalable backends.
• Design modular, maintainable multi-agent AI systems aligned with SOLID principles.
• Build high-concurrency, async Python backends for complex AI workloads with enterprise stability.
• Architect sophisticated agentic workflows using LangGraph with state persistence and error recovery.
• Design and optimize RAG pipelines: advanced chunking, hybrid search, and re-ranking.
• Collaborate on reusable AI components and internal frameworks to enhance team velocity.
• Authentication Providers (IdentityServer, Auth0 or equivalent).