ML/AI Lead
MDiet
4,000 to 5,000 USD Gross per month
Astana / Remote
Early-stage HealthTech startup. AI-powered platform reducing patient readmissions targeting the US healthcare market.
Responsibilities:
• Design and build LLM agents and pipelines for medical data curation
• Develop production RAG systems with high citation quality
• Deploy and optimize self-hosted LLM inference (vLLM)
• Fine-tune open-source models on medical data
• Create evaluation frameworks and hallucination control systems
Requirements:
• Production LLM systems (RAG + agents + inference)
• Fine-tuning open-source LLMs (LoRA/QLoRA/PEFT)
• Building data pipelines for LLM/RAG workloads
• Evaluation and hallucination reduction
• Production Python + FastAPI + async processing
Optional:
• Experience in medical / clinical NLP or regulated domains
• Cost modeling and optimization of LLM workloads
• Publications in relevant fields
Computer Vision Engineer
Aman
1,000,000 to 1,500,000 KZT NET per month
Астана / Remote
Developing a video analytics system for surveillance cameras, detecting dangerous events in real-time.
Responsibilities:
• Develop and improve human action recognition models
• Detect and classify events (fights/aggressive behavior)
• Work with streaming video (RTSP, IP cameras)
• Real-time video processing (10-25 FPS)
• Prepare and clean datasets, augmentation
Requirements:
• Confident in Python
• Experience with PyTorch or TensorFlow
• Understanding of computer vision
• Experience with OpenCV
• Knowledge of neural network training
Optional:
• Experience with video analytics
• Working with RTSP/IP cameras
• TensorRT / ONNX
AI Engineer
ОЦИБ
550,000 to 1,000,000 KZT NET per month
Астана / Office
Information security center (MSSP/SOC) with Blue Team, Red Team, R&D, Engineering teams. AI platform for SOC operations, enhancing incident analysis and automating tasks.
Responsibilities:
• Design and launch AI platform for SOC
• Integrate AI into operational processes
• Enhance incident analysis
• Automate routine tasks for analysts
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Techlead
MDiet
3,000 to 4,000 USD NET per month
Astana / Office
Early-stage healthtech startup. AI platform reducing patient readmissions in the US.
Responsibilities:
• Design and develop services, APIs, and integrations (FHIR/HL7) from scratch.
• Make key architectural decisions ensuring security and compliance (HIPAA/SOC 2).
• Establish engineering practices (code review, quality standards) and team development.
• Integrate AI components with AI Lead.
Requirements:
• system design and architecture
• process management with coding
• fluent English
Applied ML Engineer (Video / Multimodal)
AI Video Lab
1,000 to 1,500 USD NET per month
Remote
AI platform for video processing, needing a dedicated ML Engineer for the ML loop.
Responsibilities:
• Define and measure output quality
• Instrument product for labeled training signals
• Ensure pipeline quality and model orchestration
• Profile and reduce cost per video
• Manage external labeling and QC output
Requirements:
• Shipped ML in production
• Video/multimodal/computer-vision experience
• Strong evaluation instinct
• Highly autonomous
• Python fluency
Optional:
• Experience with data/eval infrastructure
• Background in social/short-form video
• Cost-optimization and GPU-utilization
Applied ML Engineer (Video / Multimodal)
AI Video Lab
1,000 to 1,500 USD NET per month
Remote
AI platform for end-to-end video processing; needs a dedicated owner for the ML loop. Build-and-own role working directly with the founder (Tech Lead) and a full-stack engineer.
Responsibilities:
• Define what “good output” means and build evaluation harness (golden sets, regression tests, quality scoring)
• Instrument product to create labeled training signals from accepts/rejects/reorders/re-edits
• Improve pipeline quality (clip selection, shot-quality scoring, pacing, scene/asset understanding, caption timing, music sync)
• Orchestrate hosted foundation models (selection, prompting, fallbacks, retries)
• Profile and reduce cost per video
• Create annotation specs, manage external labeling vendors, and QC labeled outputs (own it end-to-end) after evaluation gaps justify it
• perform fine-tuning when warranted