Mid-Senior AI / Data Engineer - Python, ML, LLMs & Data Pipeline Automation

About the Role We are looking for a skilled Mid-Senior AI / Data Engineer with a strong software engineering background to design, develop and automate data processing pipelines using traditional machine learning models and modern generative AI technologies. The ideal candidate has hands-on experience building data pipelines, integrating ML models and LLMs into automated workflows and delivering reliable, scalable solutions. We value practical engineering skills, problem-solving ability and real project experience over years of experience or specific job titles. Engineers with fewer years of professional experience are welcome to apply if they can demonstrate strong technical capabilities. Please note that some of our business requirements have recently changed. The updated project scope and expectations will be discussed during the interview. Key Responsibilities Design, build and maintain scalable ETL/ELT pipelines for structured and unstructured data. Automate data ingestion, cleansing, transformation, classification and enrichment. Integrate traditional machine learning models and generative AI/LLMs into data processing workflows. Develop Python-based backend services, APIs and automation systems. Implement AI-driven workflows for document processing, information extraction, classification and data analysis. Build reliable workflow orchestration, monitoring, error handling and retry mechanisms. Optimize pipeline performance, scalability, accuracy and operational costs. Collaborate with the client to understand evolving requirements and propose practical technical solutions. Required Qualifications Strong hands-on Python development and software engineering skills. Practical experience designing and implementing data pipelines, ETL/ELT processes and workflow automation. Experience using traditional ML models for tasks such as classification, regression, forecasting or NLP. Experience integrating LLMs such as GPT, Claude, Gemini or open-source models into real applications. Strong SQL skills and experience with relational databases. Familiarity with cloud platforms such as AWS, Azure or GCP. Ability to explain technical approaches clearly and make sound engineering decisions. Full working-hour overlap with the US client's schedule is required. Strong English communication skills, both spoken and written, for direct collaboration with the client. We are targeting mid-to-senior-level engineers. There is no strict minimum number of years required, but candidates should be prepared to demonstrate their actual technical knowledge and implementation experience during the interview process. Preferred Technical Skills Data Engineering: Airflow, Dagster, Prefect, Spark, Pandas, dbt AI/ML: scikit-learn, XGBoost, PyTorch, Hugging Face Generative AI: OpenAI API, Anthropic API, LangChain, LangGraph, LlamaIndex Databases: PostgreSQL, Snowflake, BigQuery, Redshift, vector databases Backend & Infrastructure: FastAPI, Docker, Kubernetes, CI/CD Experience with every technology listed above is not required. We care more about your understanding of engineering fundamentals and your ability to apply the right technologies to real problems. Ideal Project Experience We are particularly interested in engineers who have delivered projects involving: Automated data pipelines powered by traditional ML and LLMs. Intelligent document processing and structured data extraction. Large-scale data classification, enrichment and transformation. AI-assisted ETL workflows and data quality automation. Production-ready AI systems with monitoring, evaluation and reliability controls. Interview Process Our interview process is designed to evaluate both your communication and practical engineering abilities. 1. Non-Technical Discussion – Approach & Problem-Solving We will discuss your previous projects, how you approach engineering problems and how you communicate technical ideas. You should be able to explain your proposed solutions, architectural choices and trade-offs in clear, straightforward English without relying heavily on technical jargon. 2. Technical Deep Dive – Practical Knowledge & Experience We will explore your actual technical knowledge through detailed questions about software engineering, data pipelines, ML and LLM integration. You may be asked to explain architectures, implementation details, technical decisions, challenges and lessons learned from your past projects. We are interested in understanding what you have personally built and how deeply you understand the technologies you have used. Please be prepared for detailed technical follow-up questions. We evaluate actual engineering expertise rather than memorized answers or familiarity with technical terminology. Working Schedule & Communication The client operates during US business hours. Full overlap with the client's US working hours is mandatory. Candidates must be available for meetings, technical discussions and day-to-day collaboration during those hours. Strong verbal and written English communication is required. You should be comfortable discussing requirements, explaining solutions and providing progress updates directly to the client. Candidates may work remotely from any location as long as they can consistently meet the US working-hour and communication requirements. How to Apply Please include the following information in your proposal: A brief introduction highlighting your software engineering, data engineering and AI/ML experience. One or two real-world projects where you automated data pipelines using traditional ML models, LLMs or both. Your specific responsibilities and the main technologies used in those projects. Your availability for full overlap with US working hours. Your level of English proficiency and experience communicating directly with English-speaking clients. Your expected hourly rate and availability to start. Video Introduction & Project Presentation (Strongly Recommended): Share a link to a short video recording introducing yourself and presenting one or two projects you have personally worked on. Briefly explain your role, technical approach and key contributions. Submitting a video is optional but strongly recommended, as it can help us review your application faster and prioritize you for interview consideration. We welcome applications from capable mid-level and senior engineers alike. Our selection will be based primarily on demonstrated skills, practical experience, technical understanding and communication ability rather than years of experience alone. NO AGENCIES SHOULD BE CONSIDERED