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AI Product Engineering for Production-Ready AI Systems

TwinCore provides in-house AI software developers who build AI features directly into real products. The focus is on production use cases where AI must integrate with existing systems, data sources, and workflows, and remain stable as the product scales.

We work on AI-powered assistants and chatbots, anomaly detection in traffic and transactions, OCR and document processing, and semantic search across large text datasets. Each solution is developed as part of the product architecture, with attention to integration, performance, and long-term maintainability.
Hire Angular Developers

Meet Our Experienced AI Software Developers

  • Igor G
    10+ projects
    Igor G
    Angular developer
    Availability
    full time, part time, hourly
    Experience
    10+ years
    Angular
    Javascript
    Typescript
    HTML
    CSS
    Angular Material
    Karma
    ESLint
    CI/CD
    + other
    Detail-oriented and efficient, this developer has mastered Angular’s reactive patterns and component-based structure. A reliable choice for long-term frontend success.
    View and Hire
  • Pavel
    10 projects
    Pavel
    Angular developer
    Availability
    full time, part time, hourly
    Experience
    6+ years
    Angular
    Javascript
    Typescript
    HTML
    CSS
    Angular Material
    Karma
    ESLint
    CI/CD
    + other
    Equipped with strong skills in Angular, TypeScript, and UI optimization, this developer transforms complex tasks into intuitive digital experiences.
    View and Hire
  • Bogdan
    8 projects
    Bogdan
    Angular developer
    Availability
    full time, part time, hourly
    Experience
    8+ years
    Angular
    Javascript
    Typescript
    HTML
    CSS
    Angular Material
    Karma
    ESLint
    CI/CD
    + other
    From routing logic to UI polish, this candidate knows Angular inside out. A dependable team player ready to step into fast-moving workflows.
    View and Hire
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    Why Hire AI Engineers from TwinCore

    In-House AI Engineering Team

    Our AI engineers are full-time members of TwinCore’s team. This ensures shared standards, internal code reviews, and continuity across projects, even as requirements or priorities change.

    Production-Oriented AI Expertise

    Teams hire AI engineers from TwinCore to work on features that ship and stay in production. The focus is on integration, performance, and maintainability, not isolated models or proofs of concept.

    Remote Collaboration That Fits Your Setup

    You can hire remote AI engineers who work inside your existing tools and delivery process. Engineers join your repositories, communication channels, and release cycle without introducing parallel workflows.

    Flexible Engagement Models

    Whether you need one AI engineer or a dedicated AI development team, the engagement model adapts to your needs. This makes it easier to scale up, adjust focus, or extend delivery without restarting the collaboration.

    Cross-Domain Product Experience

    Our AI engineers have worked across different products and domains, which helps them adapt quickly to new data structures, business logic, and system constraints. This reduces onboarding time and keeps delivery moving.

    Long-Term Delivery Mindset

    TwinCore focuses on long-term cooperation rather than short-term staffing. Teams stay engaged, proactive, and aligned with product goals, which helps maintain velocity as AI features evolve over time.

    AI Engineers Focused on Measurable Impact

    TwinCore’s AI engineers work on features tied to business metrics: conversion through assistants, loss prevention via anomaly detection, faster operations through OCR, and improved discovery with semantic search.

    Predictable AI Capacity Without Long Hiring Cycles

    Hiring AI talent internally often requires long lead times and creates uncertainty at early stages. When you hire remote AI engineers from TwinCore, you get immediate delivery capacity that scales with your roadmap.

    Full Ownership and Strategic Control

    AI solutions are developed directly inside your product and infrastructure, with full ownership of code, data, and decisions.

    What Our AI Software Developers Can Deliver

    Turn AI Ideas into Product-Ready Features

    Validate and shape AI functionality at the engineering level before it hits production. Define data requirements, system boundaries, and integration points so AI features fit your product roadmap and technical reality from day one.

    Power Your Product with Machine Learning

    Add ML and deep learning models that support real workflows: anomaly detection in traffic or transactions, demand forecasting, behavioural signals, and operational analytics. Built for production use with deployment, monitoring, and iteration in mind.

    Build Custom AI-Driven Applications

    Create AI-powered applications for your business logic and functional requirements. From backend services and APIs to user interfaces and integrations, AI becomes part of the product core rather than an isolated layer.

    Extend Existing Systems with AI Capabilities

    Enhance current platforms by integrating machine learning, computer vision, NLP, and semantic search. Add new capabilities without disrupting live systems, existing data flows, or delivery schedules.

    Add Generative AI to Business Workflows

    Introduce LLM-based features such as assistants, chat-driven actions, semantic search, content analysis, and recommendation logic. Designed to work inside existing products with attention to performance, data handling, and long-term maintainability.

    Keep AI Features Stable After Release

    Maintain and evolve deployed AI functionality through monitoring, model updates, performance tuning, and security checks. Ensure AI features remain reliable as data, usage patterns, and business needs change.

    Hire AI developers in 4 clear steps

    Start with business context
    You outline what you’re building, where AI fits in the product, and what outcome matters - revenue, efficiency, automation, or decision support.
    Get engineers matched to your use case
    TwinCore assigns in-house AI engineers whose experience aligns with your stack and problem space - from LLM-powered assistants and semantic search to anomaly detection, OCR, and applied machine learning inside production systems.
    Align on delivery
    Before work starts, we align on architecture, data flow, and delivery expectations. You talk directly with the engineers who will work on your product and see how they think about trade-offs, scalability, and long-term maintainability.
    Build with continuity and accountability
    Engineers integrate into your tools and workflows and start contributing quickly. TwinCore stays responsible for continuity, documentation, and adjustments if priorities change - so progress never depends on a single person.

    Flexible Hiring Models for AI Projects

    Dedicated AI Engineer

    A full-time AI engineer embedded into your product team. Best when AI is a core part of your roadmap - assistants, automation, ML pipelines, semantic search, or analytics - and you need someone who understands your data, users, and systems deeply.

    Dedicated AI Team

    A cross-functional AI team covering model development, data pipelines, backend integration, and product-level delivery. Works well for AI-driven products, internal platforms, or when multiple AI use cases need to move in parallel - from research to production.

    On-Demand AI Engineers

    Flexible access to AI engineers for focused tasks. Ideal for adding specific expertise: improving model accuracy, integrating LLMs, building OCR pipelines, detecting anomalies, or unblocking an existing AI initiative without long-term commitments.

    Fixed-Scope AI Delivery

    Clear scope, defined outcomes, predictable timeline. Suitable when the AI task is well understood - for example, implementing document recognition, semantic search, or a chatbot with known requirements and integration points.

    Time & Material for AI Development

    A flexible engagement for evolving AI work. Best when requirements change as models are tested, data improves, or new insights appear - common in ML-heavy and GenAI-driven products where iteration is part of success.

    AI Engineers for Product Teams Solving Real Business Workflows

    We work with teams that build real products. Our Angular developers join long-term projects where performance, scalability, and maintainability actually matter.

    Logistics & Operations Teams

    Automate high-volume workflows where people still copy-paste between email, spreadsheets, and internal tools. Add assistants that create orders, pull shipment context, and surface exceptions fast, plus anomaly detection that flags issues before they become losses.

    E-commerce & Customer Ordering Flows

    Reduce friction in buying and support by moving common actions into chat-driven flows: product questions, order creation, status updates, and returns triage. Pair that with semantic search so customers and support can find the right answer in seconds.

    Document-Heavy Businesses

    Turn PDFs, scans, and forms into structured data you can actually use: OCR/OMR, extraction, validation, and routing into your systems. Great when ops teams rely on manual entry and the same small mistakes keep turning into expensive ones.

    Analytics-Driven Leadership Teams

    Spot outliers in traffic, orders, or operational metrics before they snowball into revenue impact or customer churn. Add ML signals that detect unusual patterns early, so your team investigates the right problems instead of staring at dashboards all day.
    Why Hire Angular Developers from TwinCore

    Why Product Teams Hire AI Engineers from TwinCore

    • Built with downstream impact in mind
      AI decisions are made with a clear understanding of how they affect data quality, operations, support, and revenue.

    • Comfortable inside live and legacy systems
      Work happens inside existing products, with real constraints, without stopping delivery or forcing rewrites.

    • Engineering-first AI delivery
      Models, prompts, and pipelines are versioned, testable, and treated as part of the core codebase.

    • From idea to production without resets
      AI features move through integration, deployment, monitoring, and iteration without losing momentum.

    • Clear technical communication
      Trade-offs, risks, and progress are communicated in a way business and product teams can act on.

    • Designed for long-term ownership
      AI systems remain understandable, maintainable, and extendable by your internal team over time.

    Hire AI Engineers for Your Product

    Book a call

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    Frequently Asked Questions

    How do we start if we’re not 100% sure what to build yet?

    Start with a short discovery call where you share the workflow, data sources, and the decision you want AI to improve (speed, accuracy, cost, risk). We’ll help you turn that into a clear first scope with success criteria, so you don’t pay for “research” that never ships.

    How fast can you provide AI engineers?

    In many cases, we can begin onboarding within 48 hours. The exact timeline depends on the problem scope, tech stack, access requirements, and whether the first tasks are clearly defined.

    What hiring models do you offer for AI work?

    Most teams choose one of these:

    • Dedicated AI Engineer (full-time, embedded into your team)

    • Dedicated AI Team (AI + backend + data, when scope is bigger than one person)

    • Part-time / Hourly Support (focused tasks, fixes, tuning, integrations)

    • Time & Materials (roadmap evolves, priorities change)

    • Fixed Scope Delivery (when requirements are stable and measurable)

    Can we start small before committing long-term?

    Yes. A lot of strong AI delivery starts with a single workflow: one assistant, one OCR pipeline, one anomaly detector, one semantic search surface. If it performs, scaling is straightforward because the foundations are already in place.

    Can you integrate AI into existing software without breaking our release cycle?

    Yes. We work inside existing product constraints: current APIs, auth, roles/permissions, databases, CI/CD, and release cadence. AI gets added as an increment, not a reset.

    We need AI integration via APIs. Can you design it cleanly?

    Yes. We typically expose AI capabilities through versioned endpoints, so web/mobile/internal tools can reuse them consistently. This also makes it easier to swap models, adjust prompts, or add guardrails without breaking clients.

    Can you build an AI assistant that actually helps our business?

    Yes. We can set up an AI assistant that understands your business context and is trained on your internal data, workflows, and rules. This kind of AI agent can help employees find information faster, guide them through internal processes, create or update records, and reduce manual back-and-forth.

    For customer-facing use cases, the same approach applies. The assistant can answer questions based on your real policies and data, help place orders or requests, route issues to the right team, and keep context across interactions.

    Can you build OCR/OMR pipelines for documents and images?

    Yes. We turn scans/photos/PDFs into structured text and fields, add validation where it matters, and integrate the output into your existing workflow (orders, claims, inventory, onboarding, ops queues).

    Can you automate workflows using tools like n8n or similar platforms?

    We build automations using tools like n8n when it makes sense, especially for connecting systems, triggering actions, and reducing manual work between services. This often includes syncing data between CRMs, ERPs, internal tools, databases, and AI services without building everything from scratch.

    How does AI work together with automation tools like n8n?

    AI and automation work best together. Automation handles orchestration and rules, while AI handles decisions, text understanding, classification, or suggestions. For example, an AI model can analyse an incoming request, and n8n can route it, create records, notify the right team, or trigger follow-up actions across systems. This setup keeps AI practical and controlled, instead of embedding complex logic in one place that’s hard to maintain.

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