AI Integration Engineer

7y 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 strong FIT candidate for the AI Integration Engineer role, bringing 9+ years of ML engineering experience with deep specialization in LLM integration, RAG systems, and cloud-native MLOps on AWS. their recent Tiger Analytics role directly mirrors the responsibilities of this position — building production AI pipelines, deploying LLMs, and architecting context-aware systems with FastAPI and Kafka. Minor skill gaps exist around PostgreSQL and Pinecone/Weaviate, but these are addressable and unlikely to be blockers given their adjacent experience. The primary areas to probe in interviews are their remote/async work style, PostgreSQL proficiency, and documentation discipline. their experience level may push salary expectations slightly above the posted range, which should be confirmed early in the process.

Top Strengths

✓9+ years of ML/AI engineering experience significantly exceeding the 3-year minimum requirement
✓Hands-on production expertise with RAG, LLM fine-tuning, and multi-agent system architecture
✓Strong AWS cloud infrastructure experience including EKS, SageMaker, S3, and serverless patterns
✓FastAPI and microservices experience directly aligning with the team's technical stack
✓Multilingual engineering background (Python, Rust, C++) indicating strong systems-level thinking

Key Concerns

!Incomplete LinkedIn profile raises minor concerns around professional transparency and verification
!No explicit PostgreSQL or Pinecone/Weaviate experience documented despite being required/preferred tools

Culture Fit

78%

Growth Potential

High

Salary Estimate

$95k–$115k (above posted range given 9 years experience; negotiation likely needed)

Assessment Reasoning

Huy Kaminski meets or exceeds 85%+ of the required and preferred skills for this AI Integration Engineer role. their experience with RAG systems, LLM fine-tuning, FastAPI, AWS infrastructure, Docker/Kubernetes, LangChain, and Hugging Face directly maps to the technical environment described in the job taxonomy. At 9 years of experience (7 directly relevant), they significantly surpasses the 3-year minimum and falls within the 3–8 year preferred range at the senior end. The only meaningful gaps are PostgreSQL (explicitly required but not mentioned) and Pinecone/Weaviate (they used Elasticsearch and Redis for vector search instead), both of which are learnable and do not disqualify the candidate. The incomplete LinkedIn profile reduces verification confidence but does not outweigh the strength of the resume. Recommended for immediate screening call.

Interview Focus Areas

PostgreSQL schema design and query optimization for AI workloadsDeep dive into RAG pipeline architecture decisions and trade-offs in productionAsync-first remote work experience and communication practices across EU timezonesPrompt engineering strategies and model optimization techniques used in past roles

Code Review

GoodSenior Level

No direct code samples were available for review, limiting confidence in this dimension. However, the breadth and sophistication of technologies referenced in the resume — including Rust for async multi-agent systems and production RAG pipelines — strongly suggest a senior-level engineer. A live coding or take-home assessment is recommended to validate code quality, testing discipline, and documentation habits.

PythonRustC++FastAPILangChainPyTorchTensorFlowDockerKubernetesKafkaApache AirflowMLflowKubeflow

+GitHub profile referenced (github.com/int8) suggesting open-source contributions
+Multi-language proficiency (Python, Rust, C++, Scala) indicates strong engineering fundamentals
+Architecture-level thinking evident in multi-agent system and MCP infrastructure design

-No GitHub profile data was scraped or provided for direct code quality assessment
-Cannot verify actual code cleanliness, test coverage, or documentation practices from resume alone

Experience Overview

9y total · 7y relevant

This candidate is a highly experienced ML/AI engineer with 9+ years of experience, including over 7 years directly relevant to this role. their resume demonstrates comprehensive coverage of LLM integration, RAG systems, vector search, cloud-native MLOps, and scalable pipeline design. The primary gaps are around PostgreSQL and specific vector DB tools like Pinecone/Weaviate, which are minor given their adjacent experience.

Matching Skills

LLM IntegrationPythonRAG SystemsVector DatabasesCloud Infrastructure (AWS)ML PipelinesAPI Design (FastAPI)DockerKubernetesLangChainHugging FaceOpenAI APIMLOpsMicroservices Architecture

Skills to Verify

PostgreSQL (explicit mention absent)Pinecone/Weaviate (used Elasticsearch/Redis instead)CI/CD with GitHub Actions (Jenkins mentioned but not GitHub Actions)

Candidate information is anonymized. Personal details are hidden for fair evaluation.