Senior AI Engineer /Solution Engineer — Supply Chain AI

Infinity Quest Limited; Polska; On-site; PLN 320,000 - 520,000; Full time

Job summary

Infinity Quest Limited is seeking a senior ML/AI engineer to design, build, and deploy production-grade models and data pipelines. You will work with large-scale datasets, Snowflake/Spark, and modern ML tooling to deliver end-to-end AI solutions.

The role requires 8+ years of experience, remote work from Poland, and deep expertise in Python, SQL, MLflow, and modern ML ops. You will collaborate across teams on LLM agents, data processing, and governance, with emphasis on scalable, robust

Qualifications

• Strong programming proficiency in Python with production-quality code.
• Proficient in SQL with experience on large-volume datasets (Spark/Snowflake).
• Comfortable scripting in Python and Bash for automation.
• Expertise with Git for version control, branching strategies, PR reviews.
• Familiarity with experiment tracking and model versioning (MLflow).
• Experience with Snowflake architecture and data modeling (bronze/silver/gold).
• Knowledge of RBAC, security, and data governance in data platforms.
• Hands-on ML: regression, classification, clustering, time-series forecasting.
• Experience with LLMs, AI agents, and orchestration (LangChain/LlamaIndex).
• Data processing, OCR, NLP, and CI/CD for ML pipelines (dbt+CI).
• Docker, Azure Container Apps, ACR for deployment.

Responsibilities

• Build and deploy production ML models on large-scale datasets.
• Develop data pipelines and ETL/ELT processes in Snowflake and Spark.
• Implement LLM-enabled agents and AI orchestration patterns.
• Design and optimize data models (dimensional modeling, star/snowflake schemas).
• Create CI/CD pipelines for ML/DL workflows and model deployments.
• Ensure model governance, evaluation, and monitoring in production.

Skills

Python programming

SQL

Bash scripting

Git & PR workflows

MLflow

Tools

Spark

Snowflake

Snowpipe

RBAC & security

LangChain

LlamaIndex

Azure AI Foundry

Azure DevOps

dbt

Azure ML

Job description

Location: Poland(remote)

Experience: 8+ years

Job Description:

Required Qualifications

Core Programming & Tooling

• Strong programming proficiency in Python, producing production-quality, well-structured code.
• Proficient in SQL; experience with large-volume datasets in Spark and/or Snowflake.
• Comfortable scripting in Python and Bash for automation and tooling.
• Expertise with Git for version control, branching strategies, code review, and pull request workflows.
• Familiarity with experiment tracking and model versioning (e.g., MLflow).

Snowflake

• Data lake vs data warehouse architecture (medallion architecture: bronze/silver/gold)
• API integration patterns for ingesting from source systems
• Schema evolution and handling semi-structured data
• Advanced SQL (window functions, CTEs, recursive queries, semi-structured data with VARIANT/JSON)
• Warehouse sizing, scaling, and cost optimization
• Snowpipe for streaming/continuous ingestion
• Role-based access control (RBAC), security, and data governance
• Query performance tuning (clustering keys, micro-partitions, query profiling)
• Streams and Tasks for CDC and orchestration
• Snowpark (Python/Java integration) is increasingly valuable
• Jinja templating, macros, and incremental models
• Snapshots for SCD Type 2
• Version control: Git/GitHub or Azure Repos, branching strategies
• Data modeling: Dimensional modeling (Kimball), star/snowflake schemas
• CI/CD: Automated testing and deployment for dbt models and pipelines

Machine Learning & Statistical Modeling

• Hands-on experience building and deploying ML on large-scale production datasets.
• Regression, classification, clustering, and ensemble methods.
• Time series forecasting.
• Optimization modeling (linear programming, mixed-integer programming).
• Feature engineering, data wrangling, backtesting, and statistical analysis on structured data.
• Sound evaluation practice: proper train/validation/test splits, leakage avoidance, and forward/out-of-sample validation.

LLM Applications & AI Agents

• AI agent development: building agentic frameworks using Azure AI Foundry and Azure model endpoints, including tool-use patterns and multi-step orchestration.
• Experience with orchestration frameworks such as LangChain or LlamaIndex.
• Prompt engineering and chain-of-thought design for complex, multi-step workflows.
• RAG pipeline development: chunking, vector embedding/indexing (Azure AI Search), and response generation using LLM endpoints.
• Integrating LLMs and agents with structured data sources (SQL/Snowflake), tools, and external systems/APIs.
• Grounding, citation, and evaluation techniques to control hallucination and verify model outputs.

Document Processing, OCR & NLP

• OCR and document parsing using Azure Document Intelligence / Form Recognizer (or equivalent).
• Extracting and structuring information from unstructured and semi-structured documents at scale (PDFs, scans, forms, tables).
• Text processing and NLP fundamentals: entity extraction, classification, summarization, and semantic search.
• LLM-based extraction, comparison, and term/clause analysis across long documents.
• Handling document layout, multi-page context, and noisy/low-quality inputs.
• Docker: building, optimizing, and managing container images for ML and AI applications.
• Azure Container Apps (ACA): containerized deployment, KEDA-based autoscaling, and replica management.
• Azure Container Registry (ACR): image management, automated builds, and vulnerability scanning.
• Familiarity with serverless compute such as Azure Functions for targeted tasks.

MLOps, CI/CD & Deployment

• Hands-on DevOps/MLOps experience in production environments.
• CI/CD pipeline design and maintenance for ML and LLM workloads using GitHub Actions or Azure DevOps.
• Infrastructure as Code with Terraform or Bicep for reproducible Azure environments.
• Managing environment promotion and configuration across dev, staging, and prod.
• Azure Machine Learning model lifecycle: model registry, automated retraining pipelines, model versioning, and endpoint management.
• Deployment strategies (blue-green, canary), rollback procedures, and automated testing for ML pipelines (integration, load, security).
• Deploying and scaling LLM-based applications and agent infrastructure on Azure, with attention to latency, throughput, and cost.

Monitoring, Observability & Delivery

• Delivering production-ready models and services with proper testing, validation, and monitoring.
• Azure Monitor and Application Insights: logging, tracing, custom metrics, and alerting.
• Model performance monitoring: data drift detection, prediction quality tracking, and automated alerts.
• Basic UI skills (Streamlit or Gradio) for quick prototyping and stakeholder demos.

Nice to Have

• Supply chain domain experience (demand and supply planning, inventory optimization, logistics, procurement).
• Familiarity with ERP data structures and integration patterns (Oracle, SAP, Salesforce).
• API gateway configuration (Azure API Management) and networking (VNet, Private Endpoints).
• Experience with large-scale model serving and performance optimization (GPU inference, latency tuning, cost management).