Role Description We're looking for a Senior AI Product Engineer to build the AI operating layer behind our marketplace. You take a valuable but unclear business problem — improve lead quality, cut refunds, automate a manual workflow, extract intelligence from calls — and turn it into a dependable production system end-to-end: - Investigate - Define success - Design - Build - Deploy - Improve on real usage data You are a one-person AI product team for high-leverage problems, not a spec-taker. We are expanding beyond real estate into new service-business niches, and growth is powered by reusable software and automation — not proportional growth in manual work. Every system you ship either compounds into that leverage or exposes where it leaks — no in-between. Qualifications - Strong record of building and shipping production software end-to-end, ideally in a SaaS, marketplace, or workflow product — with real users and real revenue on the line. - Strong Python and/or TypeScript, plus solid backend fundamentals: APIs, databases, auth, data models, testing, and deployment. - Hands-on experience shipping LLM-powered functionality using model APIs, structured outputs, tool/function calling, retrieval, context engineering, and agentic workflows. - Production automation experience: webhooks, events, queues, schedules, retries, idempotency, monitoring, and failure recovery — not just happy-path demos. - Practical evaluation skills: test sets, traces, quality metrics, regression checks, feedback loops, and root-causing real production failures. - Daily fluency with AI coding agents (Claude Code, Codex, Cursor, or equivalent) — you review, test, refactor, and secure generated work, not ship it raw. - English C1+; our stakeholders, users, and business owners are US-based, and communication must be sharp and clear. - AI-first by default — if you are not already using AI to 3x your own output, you will be behind the rest of engineering. - Product ownership and strong judgment in ambiguous, fast-moving environments — you clarify goals, make tradeoffs, and connect every technical decision to a business KPI. Requirements - Discover high-value product and operational problems by working directly with users, business owners, and internal teams, then frame clear success criteria before writing code. - Design and ship customer-facing AI features and internal automations across lead quality, matching, prediction, call intelligence, CRM, support, and operations. - Build agents and workflows that safely use tools, APIs, databases, calls, SMS, email, and other company systems — with human approval where risk requires it. - Create the context layer around models: instructions, retrieval, memory, structured outputs, tool definitions, permissions, and feedback loops. - Build reliable event-driven and scheduled automation using webhooks, queues, schedulers, retries, idempotency, audit logs, and graceful fallbacks. - Create evaluation datasets and automated checks that measure usefulness, accuracy, failure modes, regressions, latency, and cost — before and after release. - Investigate real production failures and improve the complete system, not just the prompt or the model response. - Turn successful solutions into reusable components and patterns that support additional iSpeedToLead niches without rebuilding from zero. - Own architecture, quality, security, and business outcomes — not just the pull request. What You'll Be Doing - Days 1–30 — Understand and ship: you have learned the product, data, and workflows, identified the best first problem with evidence, and released one useful, low-risk improvement in production. - Days 31–60 — Own the workflow: you have launched one meaningful AI product or automation with evaluation, monitoring, clear ownership, and a human fallback — running against real marketplace volume. - Days 61–90 (the bar) — Prove and repeat: the system shows measurable business impact (fewer manual hours, faster verification, lower refunds, better matching, higher conversion, or shorter time from idea to production), reliability is proven, and the solution is turned into a reusable pattern for future workflows or niches. This is not for you if - You want a prompt-only role or a research position focused on training foundation models. - You do no-code-only automation and can't own real production code. - You call yourself a product manager who delegates the build. - You ship unreviewed "vibe coding" and call it done. - You need a complete spec before you can start. - You optimize for demo quality instead of measurable business impact. Nice to Have - Voice AI, call transcription, telephony, SMS, email, CRM, or customer-support automation experience. - Lead generation, marketplaces, real estate, sales technology, or marketing automation background. - Ranking, scoring, prediction, experimentation, or data pipeline experience. - Cloud deployment, Docker, CI/CD, observability, security, and handling sensitive customer data. - MCP, vector search, LangGraph or equivalent orchestration, Temporal, n8n, or custom workflow engines. - Track record taking a product or automation from zero to one in a fast-moving startup. What We Offer - Impact: Own visible, high-value problems with direct access to decision-makers, users, and production feedback at the core of a product-led, AI-powered marketplace. - Autonomy: High ownership, low bureaucracy, and real authority to choose tools based on results and define the reusable AI architecture for multiple future niches. - Flexibility: Remote-first culture with flexible hours and async-friendly communication. 4+ hour overlap with Asia/Dubai required for weekly reviews. - Compensation: Competitive fixed base salary above the market average. - Team: Join a smart, passionate team that moves fast, supports each other, and builds cool stuff. - Time Off: 15 paid vacation days plus 5 paid sick days annually. - Growth: Unlimited learning policy — paid courses, books, and growth resources. - AI-Heavy Environment: AI is deeply embedded across prospecting, research, engineering, and workflows. - Ownership: Real decision-making power and direct ownership of your outcomes. Location Remote, 4+ hour overlap with Asia/Dubai

• Lead the strategic growth and evolution of TTEC Digital's Azure practice. • Develop and execute the Azure go-to-market strategy in partnership with sales, delivery, alliances, and executive leadership. • Build, mentor, and scale a high-performing team of Solution Architects and technical leaders. • Drive revenue growth, pipeline development, bookings attainment, and market expansion. • Create repeatable sales motions, offerings, and industry-specific solutions that accelerate growth. • Develop and strengthen executive relationships across Microsoft. • Partner closely with Microsoft field teams, partner organizations, solution specialists, and alliance leaders to generate opportunities and drive joint success. • Position TTEC Digital as a strategic Microsoft Azure, Data, and AI partner. • Collaborate with Microsoft on co-sell motions, funding opportunities, and strategic client pursuits. • Guide clients through enterprise data modernization initiatives that prepare organizations for AI adoption. • Help clients transform disparate and legacy environments into modern, cloud-based data estates. • Serve as a trusted advisor to C-suite and senior executive stakeholders. • Lead executive-level discussions regarding Azure transformation, data strategy, and AI enablement. • Shape solution strategy for complex client opportunities and oversee architecture direction across major engagements. • Partner with delivery leadership to ensure successful execution and client outcomes. • Support strategic pursuits, proposal development, and client presentations. • Partner with sales leadership to build and advance Azure opportunities. • Leverage existing client relationships to drive land-and-expand growth strategies. • Help identify opportunities across TTEC's established customer base and Microsoft ecosystem. • Contribute to pipeline generation, deal strategy, and executive relationship management.

• Serve as the primary AWS AI architecture partner for strategic clients, driving generative and agentic AI system design from discovery through production. • Lead architecture design using Amazon Bedrock (including foundation models and custom models), Bedrock AgentCore, AWS Strands Agents, and AWS AgentCore Gateway. • Design advanced RAG, Agentic RAG, and multi-agent orchestration architectures leveraging AWS-native services such as Lambda, Step Functions, API Gateway, DynamoDB, Aurora (pgvector), and OpenSearch. • Produce AWS reference architectures, architecture decision records (ADRs), and implementation roadmaps aligned to business objectives. • Validate feasibility through hands-on prototyping in Python using Bedrock SDKs, SageMaker, and serverless services. • Ensure architectures follow AWS security best practices (IAM, KMS, VPC, PrivateLink) and cost optimization principles. • Own architectural integrity from concept through production deployment on AWS. • Align solutions with AWS Well-Architected Framework pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability. • Guide clients through tradeoff decisions across model selection (Bedrock FMs vs custom SageMaker models), latency, cost, governance, and compliance. • Accelerate time-to-value through reusable AWS accelerators, Infrastructure as Code (CloudFormation/Terraform/CDK), and CI/CD automation. • Continuously evaluate emerging AWS AI capabilities (Nova Forge, Nova 2 Sonic, Bedrock updates, and new AgentCore capabilities). • Provide architectural oversight to Forward Deployed Engineers and AWS delivery teams. • Establish best practices for MLOps on AWS including model lifecycle management, monitoring, and observability using SageMaker, CloudWatch, CloudTrail, and AWS Config. • Define governance, responsible AI guardrails, Bedrock Guardrails configuration, and security controls for enterprise environments. • Mentor engineers on AWS AI service integration, distributed systems design, and secure multi-account strategies. • Make principled tradeoffs under constraints related to privacy, compliance (SOC2, HIPAA, GDPR), cost, and operational complexity. • Partner with internal product, engineering, research, and customer success teams to evolve AWS-based AI offerings. • Contribute AWS reference architectures and reusable infrastructure modules to internal accelerators. • Support pre-sales engagements including architecture workshops, AWS migration strategy, and solution scoping. • Collaborate across distributed teams and client stakeholders across North America.

• Lead cloud solution strategy and technical discovery across public, private and hybrid environments • Work closely with Sales, Product, Engineering and Customer Success • Design differentiated solutions that strengthen GTT’s position in the market • Lead technical discovery workshops across AWS, Azure, GCP and hybrid workloads • Produce high-quality solution designs, migration strategies and technical documentation • Partner with senior customer architects and engineering teams to shape scalable, secure solutions • Drive competitive positioning, clearly articulating GTT’s value and differentiation • Provide market insights to influence roadmap direction • Ensure architecture governance, accuracy and alignment with GTT’s cloud and network portfolio