Location
Remote
Required Skills and Experience
— 5+ years of full ML lifecycle experience
— Strong understanding of ML foundations
— Experience with PyTorch (preferred) or TensorFlow
— Experience with both closed-source (OpenAI, Google, Anthropic) and open-source (LLaMA, Mistral) models
— Understanding of various prompting techniques (e.g., Few-Shot, Chain-of-Thought, ReAct)
— Experience building RAG systems (e.g., LangChain, LlamaIndex) and agentic applications (e.g., LangGraph, CrewAI)
— Familiarity with MLOps practices: model versioning, deployment strategies, observability and monitoring
— Proficiency in cloud platforms: AWS (SageMaker, Bedrock), GCP (Vertex AI), or Azure (Azure ML)
— Python (required), JavaScript/Node.js (optional, for integration purposes)
— Experience with SQL (BigQuery, PostgreSQL), NoSQL (MongoDB, DynamoDB), and vector databases (e.g., Pinecone, Qdrant, pgvector)
— Hands-on experience with Docker/Kubernetes, Infrastructure as Code (e.g., Terraform, AWS CDK), and building serverless architectures
Would be a plus
— CTO / Tech Lead experience on AI products
— Experience in industries like telehealth, hospitality, or manufacturing
— Familiarity with regulations (GDPR/CCPA)
— Experience with Computer Vision projects (especially in GenAI, e.g., txt2img, img2img, txt2video generation)
— Worked with multilingual NLP systems
— Low-latency conversational AI (speech-to-text, text-to-speech)
Responsibilities
— Design and implement AI-driven applications using LLMs from different providers (e.g., OpenAI, Google, Anthropic).
— Build and deploy NLP and Generative AI models tailored for real-world use cases (chatbots, document parsing, knowledge systems).
— Architect and implement retrieval-augmented generation (RAG) systems and semantic search using vector databases (e.g., Pinecone, Qdrant, pgvector).
— Architect and implement cloud-based AI solutions with AWS (Sagemaker, Bedrock), GCP (Vertex AI) or Azure (Azure ML)
— Develop AI agents with real-time interactions utilizing speech-to-text (Whisper, Deepgram) and text-to-speech (ElevenLabs).
— Collaborate with cross-functional stakeholders to translate domain-specific needs into working AI tools.
— Ensure production-grade quality: scalability, cost-efficiency, monitoring, and fallback/human-in-the-loop systems.
— Complete discovery phases for AI-powered product development (e.g., calls with clients, implementation plans, estimations, Q&A rounds).
We offer
— All the regular HR benefits including taxes compensation;
— Remote and flexible work;
— 28 days off per year;
— Paid vacation and sick leaves;
— Open-minded and friendly atmosphere without much bureaucracy and micromanagement;
— Professional growth and development;
— Opportunity to contribute your ideas and hone your skills by taking part in interesting projects;
— Regular online company events.