CAW.Tech

About the Role

We're looking for a Prompt Engineer with hands-on experience in LLMs and AI-powered conversational experiences. This is a unique role where prompt engineering meets conversational design — you'll craft effective prompts, optimize AI behavior, and help design natural, engaging voice interactions powered by modern AI models.

You'll work at the intersection of Generative AI, prompt engineering, conversational design, and real-time voice automation, shaping the next generation of Voice AI experiences for our SaaS products.

What You'll Do

• Write, refine, test, and optimize prompts for LLM-powered voice agents handling real-time conversations.
• Experiment with models such as GPT, Claude, Gemini, and others to determine the best fit for different use cases.
• Analyze AI responses, conversation transcripts, recordings, and performance metrics to improve accuracy, reliability, and user experience.
• Design voice-first conversations that feel natural, human-like, and intuitive.
• Map conversation flows, fallback strategies, edge cases, and user journeys for voice bots and IVRs.
• Collaborate with engineers to integrate prompts into voice AI platforms and APIs.
• Balance UX empathy (tone, clarity, accessibility) with AI constraints (latency, interruptions, context handling, and accuracy).
• Experiment with A/B testing, multilingual experiences, and emerging AI capabilities.
• Document prompt libraries, conversation specifications, testing outcomes, and design decisions for consistency and scalability.

What We're Looking For

Must Have

• Hands-on experience with prompt engineering and LLM-powered applications.
• Familiarity with leading AI models such as GPT, Claude, Gemini, or similar foundation models.
• Understanding of model strengths, limitations, and trade-offs across different LLMs.
• Ability to evaluate, troubleshoot, and improve AI outputs through prompt optimization.
• Understanding of conversational workflows, AI agents, structured outputs, and tool/function calling concepts.
• Strong written communication skills with the ability to create clear, concise, and context-aware prompts.
• Ability to diagnose AI or conversation failures and improve them using data-driven insights.
• Strong collaboration skills and comfort working with engineers, product managers, designers, and clients.

Good to Have

• Experience with voice bots, chatbots, IVRs, virtual assistants, or conversational AI products.
• Familiarity with voice AI platforms such as Retell, Vapi, ElevenLabs, Deepgram, or similar tools.
• Experience with conversation analytics, evaluation frameworks, A/B testing, or prompt experimentation.
• Exposure to multilingual conversational experiences.
• Experience working on SaaS, customer support automation, AI agents, or voice automation products.

Interview Process

We run a short, collaborative exercise instead of lengthy take-home assignments. You'll have the opportunity to demonstrate your prompt engineering, problem-solving, and conversational thinking skills while working with our team in real time.

CAW We are looking for a Application Support Engineer to lead customer onboarding, deployments, and production support on the platform. This role sits at the intersection of engineering, customer success, and product and requires strong technical depth combined with excellent skills. You will work directly with enterprise customers to ensure fast, reliable deployments and long-term technical success. This is not a traditional support role; it's a high-ownership, hands-on techno-support and deployment engineering role.

The candidate will have responsibilities across the following functions:

Customer Deployment and Onboarding:

● Own end-to-end deployment of our platform for enterprise customers. ● Configure integrations, workflows, and environment-specific setups.

● Drive fast go-live and post-deployment stabilization.

Customer Interaction and Requirement Gathering:

● Act as the primary technical point of contact for customers.

● Understand customer business flows, data needs, and system constraints.

● Translate requirements into clear technical inputs for Product and Engineering teams.

Production Support (L1 and L2):

● Handle L1/L2 production support for customer-reported issues.

● Debug data, integration, performance, and infrastructure issues.

● Act as a technical escalation point during critical incidents.

Data and ETL Support:

● Understand and troubleshoot ETL pipelines and data workflows.

● Support ingestion, transformation, validation, and reconciliation of data.

● Work with APIs, databases, and file-based integrations.

Product Feedback and Documentation:

● Share real-world customer feedback, gaps, and edge cases with Product teams.

● Create deployment guides, runbooks, and internal documentation.

Requirements:

● 4-8 years of experience in customer-facing technical roles.

● Strong understanding of ETL pipelines and data workflows.

● Experience working with AWS, GCP, or Azure cloud environments.

● Knowledge of cloud networking fundamentals.

● Knowledge of Java, Spring Boot, Python, and Postgres is a must.

● Familiarity with SQL, APIs, monitoring, and logging tools.

● Strong troubleshooting and problem-solving skills.

● Excellent written and verbal communication skills.

● Experience with SaaS, FinTech, or enterprise platforms.

● Prior roles in Solutions Engineering, Deployment Engineering, or Technical Support (L2/L3).

● Startup or high-growth environment experience.

● Effective Communication: Clear and precise communication to collaborate effectively across teams.

● Curiosity and Growth: A proactive learner with a passion for continuous improvement and innovation.

About CAW Studios

CAW Studios is a Product Engineering Company of 150+ geeks.

We have built these products - Interakt, CashFlo, KaiPulse, and FastBar. And we still run their complete engineering.

We are part of the engineering teams for Haptik, EmailAnalytics, GrowthZone, Reliance General Insurance, and KWE Logistics.

We are obsessed with automation, DevOps, OOPS, and SOLID. We are not into one tech-stack - we are into solving problems.

Find us: https://goo.gl/maps/dvR6L26JUa42

Website: https://www.caw.tech/

Role

We are looking for a Full Stack Engineer who can own the entire technical stack, design systems that scale, and ship products fast. You will work across frontend, backend, and AI systems, making key architectural decisions while building a product used by real users.

This role offers high ownership, where engineers move ideas to production quickly and take responsibility for both technical decisions and product impact.

What would you do?

• Build and own the end-to-end platform using React, Node.js microservices, Python AI agents, and AWS.
• Design and implement scalable system architecture, including caching, databases, and state management between AI and UI.
• Develop AI-powered backend services and orchestrate LLM workflows using modern frameworks.
• Build highly interactive front-end experiences using modern React and real-time communication tools.
• Define and maintain engineering best practices, including CI/CD pipelines, monorepo structures, and development workflows.
• Collaborate closely with users and product teams to identify problems and ship impactful solutions.
• Continuously simplify systems by removing unnecessary complexity and keeping architecture clean.

Who should apply?

• Engineers with 4+ years of experience building and shipping production-grade products.
• Strong understanding of system design, architecture, and scalable backend systems.
• Hands-on experience with Python (FastAPI, async systems) and LLM-based applications.
• Proficiency in JavaScript / TypeScript with Node.js and modern backend frameworks.
• Experience building modern frontend applications using React (React 18+).
• Familiarity with databases such as Redis, PostgreSQL, or MongoDB, and designing scalable APIs.
• Engineers comfortable working in fast-paced environments with high ownership and minimal process overhead.

Technical Skills

• Backend: Node.js, Express, Python, FastAPI
• Frontend: React (React 18+), interactive UI development
• AI/LLM Systems: LLM orchestration, multi-model integrations
• Databases: Redis, PostgreSQL, MongoDB
• Infrastructure: AWS, CI/CD pipelines, microservices architecture
• Real-time Systems: Socket.IO, Server-Sent Events (SSE)

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

About the Role

We are looking for a senior backend engineer to build the infrastructure that powers AI agents in enterprise environments. You will work on systems that securely run, manage, monitor, and govern AI agents as they interact with users, business applications, and external tools.

This role combines backend engineering, distributed systems, AI agent infrastructure, security, and platform development.

Responsibilities

• Build and maintain the core runtime that manages AI agent execution and lifecycle.
• Develop systems for message processing, task orchestration, queue management, and recovery from failures.
• Design secure communication and execution boundaries between AI agents and the host platform.
• Build and enhance sandboxed environments for safe tool and code execution.
• Develop scalable backend services, APIs, and platform components.
• Build admin interfaces and APIs for configuring agents, permissions, approvals, and audits.
• Implement capability, access control, and permission management systems.
• Develop durable memory and state management systems for AI agents.
• Build integrations with communication platforms, business applications, and external systems.
• Improve platform reliability, observability, security, and performance.
• Collaborate closely with engineering teams to design, develop, test, and ship new capabilities.

Required Skills & Experience

• 5+ years of backend software engineering experience.
• Strong proficiency in TypeScript, Node.js, or another modern typed programming language.
• Experience building scalable, distributed, and event-driven systems.
• Strong understanding of databases, transactions, queues, background workers, and system reliability.
• Experience designing and building REST APIs and backend services.
• Hands-on experience using AI coding tools and assistants in daily development workflows.
• Understanding of AI agent frameworks, agent workflows, tool calling, context management, or agent orchestration concepts.
• Strong knowledge of system design, security principles, authentication, authorization, and access control.
• Experience debugging, optimizing, and maintaining production systems.
• Ability to work independently in large codebases and deliver high-quality solutions.

Good to Have

• Experience with AI agent frameworks or SDKs.
• Experience building developer platforms, admin consoles, or management systems.
• Knowledge of sandboxing, process isolation, containers, or secure execution environments.
• Experience with distributed systems concepts such as ordering, retries, idempotency, and fault recovery.
• Experience designing extensible architectures and integration platforms.
• Exposure to AI infrastructure, LLM applications, or autonomous agent systems.

What We're Looking For

• Strong backend engineering fundamentals.
• Security-first mindset.
• Experience building reliable production systems.
• Interest in AI agents and AI infrastructure.
• Ability to move quickly, solve complex technical problems, and work in a fast-paced environment.

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.