ML, MLOps and Data Engineering Recruitment for AI Teams

Recruiting ML Engineers, Data Scientists, LLM engineers and BizDev for AI startups and enterprise AI teams

  • We help you hire Middle+ ML engineers, Data Scientists and AI developers
  • Candidates are screened by a domain expert, not keyword-matched by a recruiter
  • Access to engineers who never post their resumes publicly
  • Ringostat
  • Promtys
  • SolveCare (TuumIO)
  • CHI Software
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What makes Ukraine’s AI talent market different

AI recruitment in Ukraine in 2026 comes down to how narrow the overlap of requirements is:

  • production experience with a specific stack (LLM, CV, recsys, NLP)
  • willingness to work in startup mode, not R&D outsourcing
  • salary expectations anchored to Tier-1 remote offers, mostly US ones, rather than to the Ukrainian market

Each of these three filters removes roughly 60-80% of candidates whose resumes contain the right keywords. What is left is a pool that does not post resumes on job boards and does not reply to mass InMail campaigns. You reach these people through market mapping and warm referrals.

 
Senior Python vs ML Engineer. A Senior Python developer with a web backend background (Django, FastAPI, REST API) is not an ML engineer.
Real machine learning work requires experience with PyTorch or TensorFlow, building training pipelines, an understanding of feature engineering, model quality metrics and A/B testing on ML systems. Hiring an ML engineer starts with a filter on production experience, not with the word “python” in a resume.
 
 
Why your old ML engineer requirements no longer work
 
Because in 2026 the ability to write code matters less than the ability to prepare data for RAG systems. A fundamentally new type of AI engineer now dominates the market: one who works not with in-house models but with LLMs (GPT, Claude, Llama, Mistral).
Among our clients the most requested skills are prompt engineering, RAG (retrieval-augmented generation), fine-tuning and vector databases (pgvector, Pinecone, Weaviate, Qdrant).
Most ML engineers have no hands-on experience with these tools: of the last 30 ML engineers we interviewed, 19 had never run RAG in production.
 
Candidates with genuine LLM production experience are mostly people who spent the last 12-18 months inside specific startups. Machine learning recruitment in 2026 means working with a very narrow active pool of candidates who can actually ship models to production.
 
The global market pulls the strongest people out.
A strong ML/AI engineer with 5+ years of experience in Ukraine has an alternative: a remote contract with a US or European company at $6,000-$10,000 per month.
A local startup struggles to compete on rate, but it can win on an interesting product, equity, a flexible schedule and the chance to shape technical strategy. AI recruitment requires understanding this logic, otherwise the role is pitched the wrong way and the candidate goes to a competitor.
 
Domain specifics.
AI for medtech, AI for retail, AI for defense and AI for finance are different problems, different models and different data standards. A candidate who spent three years building recommendation systems for e-commerce will not immediately create value for a medtech startup operating under FDA regulation.

Filled Roles and Timelines

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Role: Chief Product Officer
Filled in: 6 weeks
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Skyvia is a no-code cloud data integration platform by Devart, in operation since 2014. Within a single service it brings together data integration and synchronization, process automation, backups, data access via SQL and OData, and 200+ ready-to-use connectors to SaaS applications, databases, and storages, with no local installation.

Key responsibilities
  • Advancing the AI direction
  • Owning the product strategy and a unified roadmap across the platform’s five products
  • Consolidating the product portfolio into a coherent vision and set of priorities
  • Translating product strategy into measurable goals and metrics for the teams
  • Working with the founders on product vision and key decisions
  • Growing the product team and the discovery and delivery processes
  • Prioritizing at the intersection of market demand, data, and technical feasibility
  • Cross-functional work with engineering, analytics, marketing, and sales
Candidate requirements
  • 8+ years in product leadership at the level of CPO, VP Product, or Head of Product
  • Experience managing a multi-product portfolio in B2B SaaS
  • Proven track record of owning product strategy and roadmap at the company level
  • Experience building and growing product teams
  • Understanding of the data integration domain, ETL/ELT, APIs, and cloud infrastructure
  • Experience launching or growing an AI direction within a product
  • Command of product and business metrics, and the ability to tie them to decisions
  • English at C1 level or above for work in an international product
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Role: Sales Executive
Filled in: 19 days
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Promtys [promtys.com] is a platform that helps businesses build effective workflows with LLMs and prompts.

Key responsibilities
  • Full-cycle B2B sales through outbound lead generation
  • Working with Mid-Market and Enterprise clients across international markets (US, Europe), managing multiple stakeholders
  • Running discovery sessions and demos, building personalized Promtys use-case scenarios tailored to the client’s needs
  • Preparing commercial proposals and negotiating terms, contracts, and pricing
  • Managing pipeline and forecast in the CRM, projecting close timelines
  • Growing the portfolio of active clients through expansion and upsell
  • Maintaining a feedback loop with the product team, formalizing client requests, and influencing the AI feature roadmap
Candidate requirements
  • 2-3+ years of experience in B2B SaaS / IT product sales
  • Proven track record of closing deals in international markets (US, Europe)
  • Experience with Mid-Market, ideally with the Enterprise segment
  • Fluent English (C1+), with experience negotiating terms and contracts with native speakers
  • Understanding of AI / LLMs (ChatGPT, Claude) and prompt engineering concepts; ability to hold technical discussions with the client’s product / engineering teams
  • Experience with CRMs (HubSpot, Salesforce, and others), at the level of independently managing a pipeline
  • Focus on closing deals and hitting quota
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Role: Senior ML/NLP Engineer
Filled in: 5 weeks
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Ringostat [ringostat.com] is an AI platform for business telephony and marketing analytics that brings communications, sales, and analytics together in one system.

Key responsibilities
  • Develop Conversation Intelligence features: automatic call summarization, sentiment analysis, next-step extraction
  • Integrate and tune ASR for multilingual call streams
  • Build and optimize LLM pipelines for conversation analysis
  • Optimize quality, latency, and inference cost
  • Build evaluation: offline datasets, metrics, regression tests
  • Run A/B tests on models and prompts in production
  • Partner with the product and backend teams on CRM integrations
Candidate requirements
  • 5+ years in ML/NLP, including 1+ year with LLMs in production
  • Strong Python; PyTorch and Hugging Face Transformers are a plus
  • Hands-on experience with ASR (Whisper, Deepgram, AssemblyAI) — understanding of phone audio specifics
  • LLM engineering: prompt engineering, structured output
  • Experience A/B testing ML models in production
  • NLP task experience — summarization, sentiment, entity / intent extraction
  • Multilingual models (English required; experience with UA / RU / EU languages is a plus)
  • Experience building end-to-end ML pipelines and shipping them to production
  • Docker, basic cloud environment (AWS or GCP)
  • Understanding of quality metrics and evaluation for LLM / NLP tasks
  • English B2+
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Role: Senior MLOps Engineer
Filled in: 26 days
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Protectimus [protectimus.com] is a B2B multi-factor authentication (MFA) provider. Cloud and on-premise platforms protecting employees, customers, and corporate data, with support for various one-time password delivery methods.

Key responsibilities
  • Set up CI/CD pipelines for ML model training, validation, and deployment
  • Prepare infrastructure for login risk analysis and anomalous activity detection models
  • Containerize models and roll them out to client cloud and on-premise environments
  • Set up model monitoring in production: data drift, prediction quality, latency, alerts
  • Version models, datasets, and experiments
  • Automate retraining pipelines to keep up with evolving attack patterns
  • Partner with the ML engineer and backend team to integrate models into the MFA platform
Candidate requirements
  • 4+ years in DevOps / MLOps, including 3+ years on ML infrastructure
  • Strong Python, Kubernetes, Bash, Linux
  • Production-grade Docker
  • Real-time serving (Triton, BentoML, KServe, FastAPI under load)
  • Data drift / concept drift detection, tools like Evidently, WhyLabs, Arize, or custom production quality metrics
  • GitLab CI, GitHub Actions, or Jenkins
  • Experience with DVC, LakeFS, or similar
  • One of the cloud providers (AWS / GCP) + experience deploying to on-premise / private cloud
  • Prometheus, Grafana, basic ELK / Loki
  • Understanding of security product specifics — data sensitivity, compliance (PCI-DSS, SOC 2, GDPR)
  • Fluent English
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Role: Head of Product AI
Filled in: 2 months
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eSputnik is an omnichannel Customer Data Platform for e-commerce and retail, used daily by around 3,500 brands. It unifies customer data into a single profile and orchestrates communication across every channel at once — Email, SMS, web and mobile push, Viber, Telegram, In-App, App Inbox, and pop-ups. At the core of the product sits a proprietary AI layer — product recommendations, predictive segmentation, and content optimization, alongside an emerging AI agents direction.

Key responsibilities
  • Owning the platform’s AI strategy and roadmap across recommendations, predictive segmentation, content optimization, and AI agents
  • Growing the AI recommendations direction, with relevance and personalization across all channels
  • Advancing predictive segmentation, shipping churn, LTV, and propensity models as product features
  • Launching and owning the AI agents track that was only outlined before the hire
  • Defining the product vision for embedding AI into omnichannel orchestration (Email, SMS, web/mobile push, Viber, Telegram, In-App)
  • Prioritizing the AI backlog and balancing research against shippable features
  • Working with DS/ML and engineering teams to take models from prototype to production feature
  • Translating AI capabilities into client value through activation, conversion, and retention for e-commerce and retail brands
  • Owning the product’s AI metrics, including model impact on campaign performance and adoption
  • Partnering with leadership on AI positioning and the platform’s competitive differentiation
Candidate requirements
  • Experience in a Head- or Lead-level product role focused on AI/ML products
  • Understanding of the ML domain (recommender systems, predictive and propensity models, segmentation, LLMs, and AI agents) deep enough to own the roadmap and work with a DS/ML team
  • Track record of taking AI features from prototype to production in a SaaS or platform product
  • Background in martech, CDP, e-commerce, or retail with omnichannel communication and customer data is a plus
  • Command of product metrics and the ability to tie AI impact to business outcomes (conversion, retention, LTV)
  • Experience prioritizing a roadmap at the intersection of research and shipped features
  • Leadership in cross-functional work with DS, ML, engineering, and analytics
  • English for work in an international product serving 3,000+ brands across 23 countries

Roles we fill in the AI segment

We help seed and Series A AI startups round out their teams with engineers and with BizDev/sales hires for scaling.
In parallel we fill roles in the enterprise sector, where there is a current boom in building in-house AI teams across pharma, fintech and retail.

 
AI product startups.
The most common requests: an ML/AI engineer with experience of a specific model class (LLM, CV, NLP), a Data Engineer to build a data pipeline from scratch, a BizDev/Sales hire with fluent English. This is often the first hire after the founders, so culture fit matters as much as technical skills.
One real example is Promtys, a B2B AI content generation service, where we closed a BizDev Sales role for an AI startup in 29 days. A separate process applies to the commercial side of AI startups.
 
Enterprise AI teams.
Large companies, banks, retail, pharma and energy are building internal data/AI functions. What they look for: a Data Scientist for recommendation and scoring systems, MLOps engineers to build the infrastructure, a Head of Data or Chief Data Officer at executive level. For top roles such as CDO and Head of AI we run a separate track – Executive Search for IT.
 
The technology stack we work with:

  • LLM / GenAI: GPT, Claude, Llama, Mistral, fine-tuning, RAG, prompt engineering, vector DBs (Pinecone, Weaviate, Qdrant and pgvector), LangChain, LlamaIndex
  • Classical ML: scikit-learn, XGBoost, LightGBM, recommendation systems, scoring, churn prediction
  • Computer Vision: OpenCV, YOLO, object detection, segmentation, OCR, face recognition / re-identification
  • NLP (classical): spaCy, NLTK, transformers, named entity recognition
  • Speech: ASR (Whisper), TTS (Kokoro, Cartesia, ElevenLabs)
  • MLOps: Kubeflow, MLflow, Airflow, Weights & Biases, Docker, Kubernetes, GPU infrastructure (A100/H100, vLLM, TGI)
  • Data Engineering: Spark, Kafka, Airflow, dbt, Snowflake, BigQuery, Databricks

We build a screening checklist for each specific role. If you are shipping an LLM service, we screen for LangChain/LangGraph, vector DBs and inference optimization. If it is recsys, we screen for Spark, feature stores and A/B testing. We adapt to your stack.

Testimonials from our clients

Industries we specialize in

Recruiting for retail
Retail
Recruiting for E-commerce
E-commerce
Recruiting for iGaming / Gambling
iGaming / Gambling
C-level executive search
C-level Executive Search
Recruiting for FMCG manufacturing and sales
FMCG (Manufacturing & Sales)
Recruiting for construction
Construction & Real Estate

How we source AI talent

Channels such as Djinni and LinkedIn have limited reach for AI roles: strong AI engineers either do not publish resumes or move straight to international contracts. We use a different approach: market mapping, domain filtering of candidates by real production experience, and direct headhunting through closed AI communities.

How we run an AI search

  • A 30-60 minute intake call. Unlike a standard IT intake, we ask the CEO or CTO to describe the product in detail, which AI function the role covers, and which models or stacks are expected. Without this it is impossible to screen candidates correctly.
  • Market mapping. We build a map of every potentially suitable AI engineer in Ukraine with a relevant stack (for example LLM + RAG + production deploy). This is the foundation of machine learning recruitment in a narrow market.
  • Domain filtering. A candidate with three years of e-commerce recommendation systems is not a fit for a medtech startup. We flag this before the first call so the client does not waste time on irrelevant people.
  • Technical screening. We do not ask “do you know Python”. We ask which project went to production most recently, which models were deployed, and how specific problems were solved (LLM hallucination, model drift, inference optimization).

 
Ukrainian AI market data:

  • Senior Python ML/AI Developer – 298 candidates for 73 open roles on Djinni (Q2 2026)
  • Middle+ ML/AI engineers with genuine production experience – roughly 170-220 in Ukraine (based on interviews conducted over the last two quarters)
  • Senior ML Engineer salary range – $4,500-7,000 per month (Djinni Salary report)
  • Tier 1 remote contracts – $8,500+ per month (Indeed, Glassdoor salary reports)

A Ukrainian startup will not win on rate, but it can win on product, equity and influence over technical strategy. A recruiter who cannot sell that will never get these candidates to engage.
As an AI recruitment agency we work on a success fee basis: you pay once the candidate starts.
90-day free replacement guarantee.
 
Book an intro call and we will work out whether the role can realistically be closed in 4-10 weeks and what the candidate pool should look like.

About Us

Alexander
Founder & CEO
Oleksandr
Founder of BestHeads (since 2017) and a marketing agency. Focus on executive search and digital recruiting; deep understanding of P&L, C-suite roles, and growth drivers.
Violetta
Head of Recruitment / Partner
Violetta
Coordinates the recruiting team, plans workload, oversees quality and timelines. 10+ years in recruiting.
Eugenia
Client Success Manager
Eugenia
Takes care of clients with attention to every detail of the process: handling requests, scheduling meetings, managing onboarding paperwork.
Makar
Recruiter
Makar
Focused on individual contributor roles and sales positions: builds candidate pools fast; always hits SLAs and deadlines.
Marina
Recruiter
Marina
Sourcing and screening across marketing, finance, and sales; a 94% fill rate across the searches she runs.
Marina
Senior HR Consultant
Marina
Recruits key specialists for IT, HR, and finance; top-level expertise; 10+ years. Excellent soft skills.
Alexey
Recruiter
Alexey
Executive search in finance, economics, and accounting; roles include CFO, Chief Accountant, legal counsel, and sales managers.
Victoria
Senior HR Consultant
Victoria
Fills complex roles in marketing and healthcare; supports candidate onboarding; 10+ years in HR.

Frequently Asked Questions

Kyiv, Palats Ukraina metro
34b Predslavynska St., office 405
For clients: info@bestheads.in.ua
Resumes: cv.1603919240@m.persiahr.com
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