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Hire AI Developers Who Ship Working Features

Add engineers who turn LLMs into reliable product features: retrieval search, assistants, document processing and agents, built with evaluation and cost control.

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AI engineering that goes beyond the demo

Large language models make it possible to add search, summarisation, extraction and conversational features that were impractical a few years ago. Getting a prototype working is easy. Getting it accurate, affordable, secure and maintainable in production is the real work, and that requires engineers who understand both software and model behaviour.

Product companies hire our AI developers to add assistants and smart search to SaaS platforms. Operations teams use them to automate document handling and support. Others need an engineer to rescue a prototype that hallucinates, costs too much or cannot handle real traffic. The common thread is a need for shipped features rather than slide decks.

Our AI developers design retrieval pipelines, prompts and tool calls around your data, then measure quality with evaluation sets before launch. They choose between hosted models and open-source options based on accuracy, cost and privacy needs. Nexzem has built its own AI calling product, NexCall, so we know what production AI demands.

Evaluate before you ship

Change the prompt and turn retrieval on or off, then rerun a sample test set to see which answers pass.

Build a team with AI Developers

Pick roles and seniority, choose what they will work on, then send the brief. We come back with matching profiles.

AI Developers, by seniority
  • Junior AI Developer
    0
  • Mid-level AI Developer
    1
  • Senior AI Developer
    1
Round out the team
  • Machine Learning Engineer
    0
  • Data Scientist
    0
  • Python Developer
    0
What they will work on

What our AI Developers can do for you

AI developers to build LLM features, RAG search, chatbots and AI agents into your product, with evaluation and guardrails.

  1. 01

    LLM product features

    Summaries, drafting, classification and extraction features built into your app using OpenAI, Anthropic Claude, Google Gemini or open-weight models, with fallbacks and usage tracking.

  2. 02

    RAG search and knowledge bots

    Retrieval pipelines that index your documents, tickets or product data in a vector store and answer questions with cited sources and access control.

  3. 03

    AI agents and tool use

    Agents that call your APIs, directly or through MCP servers, to look up orders, book appointments or update records, with clear limits, approval steps and full action logs.

  4. 04

    Document AI pipelines

    Extraction of fields from invoices, contracts, forms and IDs into structured data, combining OCR, LLMs and validation rules with human review where needed.

  5. 05

    Conversational assistants

    Chat and voice assistants for websites, WhatsApp and phone lines that hand over to humans gracefully when they reach their limits.

  6. 06

    Evaluation and guardrails

    Test sets, automated scoring, prompt versioning, PII filtering and output checks so quality can be measured and regressions caught before release.

  7. 07

    Cost and latency tuning

    Model selection, caching, prompt compression and batching to bring per-request cost and response time within your budget, with dashboards that show spend per feature.

Why hire AI Developers through Nexzem

  • Engineers, not prompt tinkerers

    We assess software engineering, retrieval design and evaluation skills, so your AI developer writes production code, not just prompts.

  • Trial on a real use case

    Use the short trial to build a slice of a real feature, giving you evidence of quality before committing.

  • Data handled with care

    NDA on request, your cloud accounts and API keys, and options for private or self-hosted models when data cannot leave your control.

  • Built by a team that ships AI

    Our in-house work on products like NexCall and NexChat gives our developers practical experience with live AI workloads.

  • Flexible as your AI plans evolve

    Start with one AI developer for a pilot and add data or backend engineers as the feature proves its value.

How to vet an AI developer

Many developers can call a language model API; far fewer can build an AI feature that stays accurate, safe and affordable in production. Ask candidates how they grounded answers in company data with retrieval-augmented generation, how they measured quality and what they did when the model produced wrong or harmful outputs. Specific stories about failures and fixes are the best signal.

Engineering discipline matters as much as model knowledge. Look for experience with prompt versioning, evaluation sets that run on every change, structured outputs, function calling with permission checks, rate limits, cost tracking per feature and logging that respects privacy. Developers should also know when traditional software or a simple rule beats an AI model.

Data handling deserves direct questions. Ask how they protected sensitive data sent to model providers, handled personal information, enforced document permissions in retrieval and chose between hosted APIs and self-hosted open models for compliance reasons. Clear answers here protect both customers and the business.

  • Has shipped AI features used by real customers.
  • Builds evaluation sets and measures quality continuously.
  • Designs retrieval pipelines that respect permissions.
  • Controls latency and cost per request.
  • Applies guardrails and human review where needed.
  • Understands data privacy with model providers.
  • Writes clean, tested code in Python or TypeScript.

Interview questions we use for AI developers

Our questions focus on building reliable AI systems rather than model trivia. Candidates design a feature for a realistic scenario, such as a support assistant over company documents, and explain how they would build, evaluate, secure and operate it once real customers start using it daily.

Strong answers start with the problem and success metric, propose the simplest approach that could work and include evaluation before launch. Our RAG vs fine-tuning comparison reflects the kind of trade-off reasoning we expect candidates to explain clearly. We also ask how they would explain limitations to stakeholders.

  • How would you build a question-answering assistant over our internal documents?
  • How do you measure whether answers are correct and grounded?
  • When would you fine-tune a model instead of using retrieval?
  • How do you stop an assistant from revealing data a user should not see?
  • How do you reduce cost and latency for a high-volume AI feature?
  • How would you defend against prompt injection in tool-using agents?

Onboarding an AI developer in the first two weeks

In week one, the developer learns the business problem, the data available and any existing AI experiments. They review data access, privacy constraints and model provider agreements, and build an initial evaluation set with your domain experts, since measuring quality comes before building features.

In week two, they deliver a working prototype on real data, measured against the evaluation set, with a short report on accuracy, cost and risks. For broader programs, our AI development services cover discovery, production engineering and monitoring after launch.

Hiring AI Developers: from first call to first commit

Every stage has an owner and an exit, so you always know where your hire stands.

  1. Stage 1

    Clarify the use case

    We discuss the problem, available data, privacy constraints and how success will be measured.

  2. Stage 2

    Match AI engineers

    You receive profiles with relevant LLM, retrieval or agent experience and examples of shipped work.

  3. Stage 3

    Technical interview

    Discuss architecture choices and evaluation approach with candidates, or review a short exercise.

  4. Stage 4

    Pilot during the trial

    The developer builds an early version on your data and shares evaluation results.

  5. Stage 5

    Production and iteration

    Continue monthly to harden, launch and improve the feature, with monitoring and cost reports.

Where AI Developers make a difference

  • Support assistant over company knowledge

    A software company adds an AI developer who builds an assistant that answers customer questions from help articles and past tickets, with citations, permission-aware retrieval, escalation to human agents and quality measured every release.

  • Document extraction for operations

    An operations team processing invoices, contracts or forms gets an AI developer who builds extraction with language models and validation rules, routing low-confidence results to staff and measuring accuracy against verified samples.

  • AI features inside a SaaS product

    A SaaS company adds summarization, smart search and drafting features to its product, with an AI developer handling tenant data isolation, cost limits per plan and evaluation suites that catch quality regressions.

  • Internal copilot for sales teams

    Sales staff get an assistant that summarizes account history, drafts follow-up emails and answers product questions from approved content, built by an AI developer with CRM integration and logging for review.

Tools our AI Developers work with

Proven, well-supported tools chosen for your scale, budget and team, never for novelty.

  • Python
  • LangChain
  • Claude
  • Gemini
  • Hugging Face
  • PyTorch
  • PostgreSQL
  • Next.js

Developers who work alongside AI Developers

All 46 developer roles

More in AI & Emerging

Hiring AI Developers: FAQs

Something else on your mind? Ask a consultant and get a reply within one business day.

How much does it cost to hire an AI developer?

It depends on the complexity of the use case, required seniority, data and privacy constraints, and engagement length. Engagement is monthly per developer after a short trial, and we confirm pricing after a free consultation.

Which AI models do your developers work with?

They work with OpenAI, Anthropic Claude, Google Gemini and open-source models from Hugging Face, and choose based on accuracy, cost, latency and data residency needs.

How do you stop the AI from making things up?

We ground answers in your data with retrieval, require citations, restrict scope, add validation checks and measure accuracy on test sets before launch.

Is our data used to train public models?

We configure providers with data controls that suit your policy and can use private deployments or self-hosted models where required. We sign NDAs on request.

Can an AI developer work with our existing engineering team?

Yes. They join your repositories and sprints, and work alongside your backend and frontend engineers to integrate AI features cleanly.

How long does a first AI feature take?

A focused pilot can often be shown within the trial period. Production readiness depends on data quality, integrations and the level of accuracy required.

Can an AI developer work with open-source models instead of paid APIs?

Yes. Our AI developers work with hosted models and open-weight models that can run on your own infrastructure. They compare options on quality, cost, latency and data requirements using your real tasks, then recommend the best fit.

How do AI developers measure whether a feature is good enough to launch?

They build an evaluation set from real examples with expected answers, agree success criteria with your team and score each version automatically and with human review. Launch decisions are based on those results rather than impressions from a few demos.

We work with clients across the USA, UK, Australia, UAE, New Zealand and India.

Where we work

Tell us who you need on your team.

Share the role, stack and start date. We reply within one business day with matching profiles and next steps.

Book a free consultation+91-7302622997