Hire AI and ML Developers in Turkey

Last updated: November 11, 2026

Hire AI, machine learning and LLM developers in Turkey. Many companies have moved past AI experiments and now need engineers who can turn models into reliable features: document assistants, classification and forecasting, search, and automation that fits existing systems and data rules. Our AI/ML developers join your team or deliver a defined AI project, working in your cloud and repositories with a large daily overlap with European working hours.

This page explains when you need AI/ML engineers, typical projects, a seniority matrix, the ecosystem we work with, how we vet candidates, KPIs, pricing factors and how data and IP are protected. For other roles, go back to hire developers in Turkey.

Who an AI / ML / LLM developer is, and when you need one

An AI developer combines software engineering with data and model work. Depending on the project, that means building retrieval-augmented generation (RAG) systems on top of large language models, training or fine-tuning classical machine learning models, building data pipelines, or putting models into production with monitoring (MLOps). You typically need one when a proof of concept has to become a product, when business teams want to automate document-heavy work, or when you have data that could drive better forecasts or decisions.

Typical problems in corporate AI projects

A promising demo that never makes it into production
Hallucinated or unverifiable answers from LLM features
Unclear rules for which data may be sent to external model providers
Model quality that drops over time without anyone noticing
Unexpected inference costs as usage grows

Typical AI projects

Project type	Example scope
Document assistants (RAG)	Search and question answering over internal documents with citations, access control and evaluation
Process automation	Classifying and extracting data from emails, invoices, contracts or forms, with human review steps
Predictive models	Demand forecasting, churn prediction, scoring and anomaly detection
Computer vision	Image classification, object detection, OCR and quality inspection
AI features in products	Summaries, recommendations, semantic search and agents integrated into your application

Seniority matrix

Level	Typical experience	Independence	Expected skills
Junior	0–2 years	Supervised experiments and data tasks	Python, pandas, scikit-learn basics, notebooks, Git
Mid-level	2–5 years	Owns a model or LLM feature	LLM APIs, RAG pipelines, PyTorch or TensorFlow, evaluation, FastAPI services
Senior	5+ years	Owns AI solution design	System design for AI, fine-tuning, MLOps, cost and latency optimisation, security
Lead / AI architect	8+ years	Owns AI strategy and governance	Use-case selection, build-versus-buy decisions, governance, risk and compliance alignment

Skills and ecosystem

Languages, frameworks and libraries

Category	Examples
Languages	Python, plus TypeScript, Java or C# for integration
ML frameworks	PyTorch, TensorFlow, scikit-learn, XGBoost, Hugging Face Transformers
LLM tooling	OpenAI, Anthropic, Azure OpenAI, open-weight models; LangChain, LlamaIndex, structured outputs and tool calling
Serving	FastAPI, vLLM, ONNX Runtime, Triton

Data and infrastructure

Category	Examples
Vector and search	pgvector, Qdrant, Weaviate, Elasticsearch or OpenSearch
Data pipelines	Airflow, dbt, Spark, cloud data warehouses
MLOps	MLflow, Weights and Biases, model registries, monitoring for drift
Cloud	AWS, Azure, Google Cloud, GPU instances, on-premise deployments where data must stay local

Technical assessment: how we vet candidates

Interview topics

Designing a RAG system: chunking, retrieval, re-ranking and citation
Evaluating model output: test sets, metrics and human review
Choosing between prompting, fine-tuning and classical ML
Data privacy: what can leave your environment and how to anonymise
Productionising models: latency, cost, monitoring and fallbacks

Practical task

Candidates build a small question-answering service over a document set with an evaluation script, or train and evaluate a model on a sample dataset. We look at the reasoning behind choices, the evaluation and the code quality.

Code review criteria

Criterion	What we look for
Reproducibility	Pinned dependencies, versioned data and parameters
Evaluation	Clear metrics and test sets, not only manual impressions
Engineering quality	Tests, typed interfaces, error handling, no secrets in notebooks
Safety	Prompt injection awareness, output validation, access control

Typical team compositions

Scenario	Suggested composition
First LLM feature in an existing product	1 senior AI engineer working with your backend team
Document automation project	1 AI engineer, 1 backend developer, 1 QA engineer, part-time data engineer
AI platform or several use cases	1 AI architect, AI and data engineers, MLOps support

Engagement and delivery models

Our AI/ML developers work remotely, on-site or in a hybrid setup. Remote work is common; on-site or hybrid is useful when data cannot leave your premises or during discovery workshops with business teams.

Dedicated specialist (staff augmentation): one or more AI/ML developers join your team full time, follow your processes and report to your lead.
Dedicated team: a cross-functional team with a lead, sized from 1 to 200+ people, that owns a product area or a backlog.
Project-based delivery: a defined scope with milestones and acceptance criteria, delivered by a team we manage.
Extended coverage: 7/24 shift plans for support, monitoring or release windows where the work needs it.

Process and typical timeline

Step	What happens	Typical duration
1. Discovery call	We clarify scope, stack, seniority, time zone overlap and working model	1–2 days
2. Shortlist	We share pre-screened AI/ML profiles with our technical assessment notes	About 1 week
3. Your interviews	You interview the candidates; a trial task can be added	A few days
4. Contract and onboarding	Service agreement, NDA and DPA signed, access set up, equipment ready	Typical start in 1–2 weeks
5. Ongoing delivery	Regular check-ins, performance reviews and a replacement process if the fit is not right	For the agreed term

In-house, freelancer or staff leasing with 360 Soft

Criterion	In-house hire	Freelancer	Staff leasing / outsourcing with 360 Soft
Time to start	Often months including notice periods	Fast, availability varies	Typical start in 1–2 weeks
Employment admin	On you	On the freelancer	Handled by 360 Soft in Turkey
Continuity	Depends on retention	Risk of sudden unavailability	Replacement process included
Scaling up or down	Slow	One person at a time	From 1 to 200+ people
IP and confidentiality	Employment contract	Needs careful drafting	IP assignment and NDA in the B2B agreement

KPI and SLA table

Indicator	What it measures	How to set the target
Answer or prediction quality	Accuracy, F1, groundedness or human rating on a test set	Agree a baseline in the pilot, then targets per release
Automation rate	Share of cases handled without manual work	Define with the business owner, keep human review where needed
Latency	Response time of the AI feature	Agree per use case
Cost per request	Inference and infrastructure cost per transaction	Monitor monthly and optimise
Drift alerts	Changes in input data or model performance	Monitored continuously

Hire AI and ML Developers in Turkey — overview

Factors that drive the price

Factor	Effect
Seniority	AI architects and senior ML engineers cost more
Problem type	Fine-tuning, computer vision or on-premise models need more specialised profiles
Data situation	Messy or unlabelled data adds data engineering effort
Infrastructure	GPU and model provider costs are separate from staffing costs
Term and team size	Longer terms and larger teams are priced differently

We do not publish fixed prices; you receive a written quote based on your use case.

Risks and mitigations

Risk	Mitigation
Hallucinations	Retrieval with citations, output validation and evaluation sets
Sensitive data sent to external providers	Data classification, anonymisation, private or on-premise models where required
Model quality drift	Monitoring, scheduled re-evaluation and retraining
Prompt injection and misuse	Input filtering, least-privilege tools, human approval for actions
Developer not the right fit	Replacement process in the contract

Security, data protection, NDA and IP

You sign a B2B service agreement with 360 Soft; the specialists are engaged by us in Turkey. All work product is assigned to you under an IP assignment clause, and every team member signs confidentiality and IP undertakings before getting access. Where personal data is processed, we sign a GDPR data processing agreement (DPA) with Standard Contractual Clauses, or the UK IDTA for UK clients, and we comply with Turkey’s data protection law KVKK. Training data, prompts, fine-tuned models and evaluation sets created for you are your property. Where AI systems affect people, rules such as the EU AI Act and sector regulations in your country may apply, and we design documentation and human oversight accordingly.

Legal note

This page is general information, not legal advice. Contract terms are agreed per project. Our team operates under Turkish law and Turkish sector rules; the local rules of your own country (for example on data protection, sector regulation or contractor classification) also apply, so we recommend a review by your own counsel.

Illustrative scenario: an internal document assistant

Illustrative scenario, not a client case. An industrial company wants staff to find answers in thousands of manuals and procedures. An AI engineer builds a RAG service that respects existing document permissions, cites sources in every answer and logs feedback. A test set of real questions is used to compare retrieval settings before launch, and the assistant is rolled out department by department.

Checklist for choosing an AI developer

Have they taken an AI feature into production, not only built demos?
How do they evaluate quality, and can they show an evaluation set?
Do they understand your data protection constraints?
Can they integrate with your systems and identity provider?
Can they estimate and control inference costs?
Are IP assignment, NDA and the DPA signed before data access?

Selected corporate projects

Some of the projects 360 Soft teams have been involved in: CarrefourSA and MediaMarkt robotic AI interface. For all projects, see our case studies.

Frequently asked questions

Is our data used to train public models?

No. We configure model providers so that your data is not used for training, or use private and on-premise models when required.

Can the AI run on our own servers?

Yes. Open-weight models can be deployed on your servers or private cloud when data must stay in your environment.

Do you work with ChatGPT, Claude or other LLMs?

Yes. We work with major commercial model APIs and open-weight models and choose based on quality, cost and data rules.

Can you take a proof of concept to production?

Yes. We add evaluation, monitoring, security and integration so that the prototype becomes a maintainable service.

Do we need a lot of data to start?

Not always. Many LLM use cases work with your existing documents; predictive models usually need historical data.

How do you prevent wrong answers?

With retrieval and citations, output validation, evaluation sets and human review for high-impact decisions.

How quickly can an AI developer start?

We typically share pre-screened profiles within about a week, with a start in 1–2 weeks after your approval.

Who owns the models and code?

You do. Code, prompts, fine-tuned models and datasets created for you are assigned to you.

What about AI regulation?

We document data sources, purposes and human oversight so you can meet obligations such as the EU AI Act; your legal team should confirm the classification.

What if the developer is not the right fit?

A replacement process is part of the agreement.

Hire AI/ML developers with 360 Soft

Tell us about your project, your stack and when you need someone, and we will send matching profiles and a written quote. Use our contact page, call +90 850 335 10 87, message us on WhatsApp or email info@360-soft.com. For broader needs, see IT outsourcing services.

Need AI/ML developers? Tell us what you need and we will share matching profiles and a written quote. WhatsApp · +90 850 335 10 87 · info@360-soft.com

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