[Hiring] Fullstack AI/ML Developer @HiQo Solutions

Role Description

We're looking for a versatile AI/ML Specialist (Generative & Multimodal) who can translate business problems into data-driven solutions and deliver models to production in close collaboration with our team. You'll work across data exploration, modeling, and integration, with a strong emphasis on modern AI from classical machine learning to generative models and LLMs.

In this role, you'll build and operate end-to-end ML systems, owning data pipelines, modeling, deployment, monitoring, and iteration. The ideal candidate combines solid software engineering fundamentals with practical machine learning expertise and hands-on experience in MLOps and cloud environments.

Location: Poland (office in Krakow/Wroclaw or 100% remote)

Responsibilities

• Translate business problems into ML/AI solutions and measurable success criteria.
• Build reliable data pipelines (batch/stream) for training and inference; implement data validation and quality checks.
• Develop, train, and evaluate models (classical ML and deep learning) with reproducible experiments.
• Design and ship production services (APIs, batch jobs, streaming consumers) with automated tests and observability.
• Establish and maintain MLOps foundations: versioning (code/data/models), experiment tracking, model registry, CI/CD, and automated deployments.
• Monitor production systems (latency, throughput, cost, model performance, drift) and implement retraining/rollbacks.
• Apply modern AI techniques: LLM integrations including multimodal, retrieval-augmented generation, visual embeddings for RAG, prompt design and evaluation, guardrails.
• Optimize cost and performance (profiling, batching, caching, quantization, GPU utilization) and ensure reliability.
• Collaborate with product, data, and engineering stakeholders; document designs and decisions.

Qualifications

• 3-5+ years building ML-powered products with production ownership (data - model - deployment - monitoring).
• Strong Python and software engineering fundamentals: clean code, testing, logging, type hints, code reviews, modular design.
• Proficiency with ML/DL stack: scikit-learn; PyTorch or TensorFlow; pandas/NumPy; solid grasp of evaluation metrics and experiment design.
• SQL and data modeling; experience with warehouses/lakehouses (e.g., BigQuery/Snowflake/Redshift) and ETL/ELT tools.
• Production experience with image/video ML: classification, object detection, segmentation, tracking, OCR/document AI.
• CV frameworks and libraries: OpenCV; PyTorch (torchvision, timm) or TensorFlow/Keras; YOLO/Detectron2/MMDetection; augmentation with Albumentations.
• Orchestration and pipelines: Airflow/Prefect/Dagster or similar.
• Containers and deployment: Docker; basic Kubernetes or serverless; API frameworks (FastAPI/Flask).
• Cloud experience (AWS/GCP/Azure) including storage, compute, networking, and IAM basics.
• MLOps tooling: experiment tracking and model management (MLflow, Weights & Biases), model registry, artifact/version control.
• Monitoring/observability: metrics, tracing, and alerting (Prometheus/Grafana/CloudWatch/Datadog); model drift monitoring.
• Practical AI/LLM experience: using hosted APIs or open-source models, embeddings/vector databases (FAISS/Pinecone/pgvector), RAG patterns, safety/guardrails.
• Clear communication and the ability to scope, estimate, and deliver incrementally.
• Education: BS/MS in Computer Science, Data Science, Statistics, Engineering or equivalent practical experience.
• English Proficiency at least B2 level.

Requirements

• Infrastructure as Code (Terraform/CloudFormation), Helm, KServe/SageMaker/Vertex AI/Azure ML.
• Streaming systems (Kafka/Kinesis/Pub/Sub) and real-time inference.
• Feature stores, data contracts, and data governance.
• Multimodal and generative vision: CLIP/BLIP/ALBEF, Vision Transformers, diffusion models (inpainting, super-resolution), visual RAG.
• Performance tuning: ONNX/TensorRT, quantization, distillation, GPU scheduling; Numba/Cython.
• Security and compliance basics for ML systems, PII handling, secrets management.
• A/B testing, causal inference, and product analytics.

Benefits

• Flexible working time - you can agree on it within the team.
• Necessary tools and equipment.
• Communication in English - only foreign customers, and international Teams.
• Simple structure and 'open door' way of communication.
• Full-time English teachers.
• Medical insurance for employees.
• HiQo University - internal education and training programs.
• HIQO COINS - We have a system of rewarding employees for extracurricular activities.