Chatbot Developer

4y relevant experience

Qualified

For hiring agencies & HR teams

EU engineers, ready to place with your US clients

Pre-screened on AI. Remote B2B contracts. View 5 full profiles free — AI score, skills report, interview questions included.

Executive Summary

This candidate is a seasoned Python and GenAI engineer whose resume profile is a strong match for the Chatbot Developer role, anchored by direct RAG chatbot development at Tempus AI and broad LLM integration experience. their 10-year trajectory from full-stack engineering to AI/ML positions him well above the typical mid-level candidate. The primary risk is verification — an empty LinkedIn profile and absent GitHub portfolio mean their strong resume claims are unsubstantiated by external signals. A structured technical interview focused on their chatbot and RAG work is essential to confirm depth. If their experience checks out, they represents a high-value hire who could operate at the senior end of the mid-level salary band and grow into a technical lead capacity.

Top Strengths

✓Direct RAG chatbot development experience in a production healthcare environment (Tempus AI)
✓Strong LLM integration expertise with OpenAI GPT, Gemini, Llama3, and prompt engineering
✓Python full-stack depth across FastAPI, Flask, Django with 10 years of progressive engineering growth
✓Cloud-native experience across AWS, GCP, and Azure supporting scalable, production-grade deployments
✓Cross-domain experience in healthcare, telecom, and fintech demonstrating adaptability and problem-solving breadth

Key Concerns

!Empty LinkedIn profile and no GitHub make independent verification of claims difficult — resume credibility relies entirely on interview performance
!Poland-based candidate with US-only employer history raises questions about employment arrangement history that should be addressed early

Culture Fit

72%

Growth Potential

High

Salary Estimate

$70k-$85k (mid-to-senior positioning, Poland-based may influence expectations)

Assessment Reasoning

This candidate is scored as FIT (82/100) based on strong alignment with the core technical requirements: Python, FastAPI, LLM integration, NLP, prompt engineering, RAG systems, and API development. their resume explicitly describes building a RAG-based medical chatbot and integrating OpenAI APIs in production — the two most critical capabilities for this role. they meets or exceeds the 3-7 year experience range for AI/ML engineering. The score is tempered rather than higher due to three factors: (1) vector databases are not explicitly listed despite being a preferred qualification, (2) LinkedIn data is unavailable for cross-verification, and (3) no code samples exist to validate technical depth. These are verification gaps rather than skill gaps, making the FIT decision appropriate with a moderate confidence level of 78%. A technical screening call and coding assessment are recommended before extending an offer.

Interview Focus Areas

Deep dive into the RAG-based medical chatbot at Tempus AI — architecture decisions, chunking strategy, embedding models, retrieval mechanisms, and production challengesVector database experience — probe whether Pinecone, Weaviate, or alternatives were used despite not being listed on resumePrompt engineering methodology — ask for concrete examples of prompt iteration, evaluation, and optimizationVerify remote work history and employment arrangement with US companies while based in PolandLangChain usage depth — orchestration patterns, agent design, memory management in conversational AI

Code Review

FairSenior Level

No GitHub or code portfolio was provided, making it impossible to directly assess code quality, testing discipline, or engineering craftsmanship. Based on resume narrative alone, Marian describes senior-level architectural ownership and testing integration, but these claims require technical interview validation. A coding assessment or take-home project is strongly recommended before advancing.

PythonFastAPIFlaskDjangoLangChainOpenAI APIReact / Next.jsDockerKubernetesPostgreSQLMongoDBRedisKafkapytest

+Resume describes architectural decisions (RAG system design, microservices, CI/CD pipelines) suggesting senior-level technical ownership
+Mentions TDD and testing integration with pytest into CI/CD pipelines indicating code quality awareness

-No GitHub profile provided — cannot assess actual code quality, style, or open-source contributions
-No portfolio or public code samples available to validate claimed technical depth
-Cannot verify proficiency depth across the broad list of technologies claimed without code evidence

Experience Overview

10y total · 4y relevant

Marian presents a strong technical profile with directly relevant GenAI and Python engineering experience, including a RAG-based medical chatbot built at Tempus AI. their 10-year career spans full-stack and AI/ML development across healthcare, telecom, and fintech with reputable employers. The primary gap is the absence of explicitly mentioned vector database experience, and the geographic discrepancy between their Poland residence and US employer history warrants clarification.

Matching Skills

PythonFastAPILLM Integration (GPT-3.5, Gemini, Llama3, OpenAI)NLPRAG SystemsPrompt EngineeringAPI DevelopmentMachine LearningLangChainFlaskDjangoDockerKubernetesAWS / GCP / AzurePostgreSQL / MongoDBCI/CD pipelinesMicroservices Architecture

Skills to Verify

Vector databases (Pinecone, Weaviate, Milvus) — not explicitly mentionedLlamaIndex — not explicitly listedProduction SaaS chatbot deployment specifics

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