کوکافه | KooCafe

کامپیوتر، فناوری اطلاعات و اینترنت ۵۱ تا ۲۰۰ نفر koocafe.ir

فرصت‌های شغلی ۰

استخدام Senior AI/ML Engineer – GenAI

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دسته‌بندی شغلی

وب،‌ برنامه‌نویسی و نرم‌افزار
موقعیت مکانی

تهران ، تهران
نوع همکاری

تمام وقت دورکاری
حداقل سابقه کار

بیش از شش سال
حقوق

از ۱۷۰,۰۰۰,۰۰۰ تومان

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Important:
Please send your CV in English.

Job Description

We are seeking a highly skilled and proactive Senior AI/ML Engineer – GenAI to join our dynamic team.

As a Senior AI/ML Engineer – GenAI at Koocafe, you will design, develop, and deploy enterprise-grade AI solutions focused on Generative AI, Large Language Models, Retrieval-Augmented Generation, Agentic AI systems, scalable ML pipelines, and cloud-native AI applications.

Responsibilities

Design, develop, and deploy Generative AI applications using foundation models such as GPT, Claude, Gemini, Llama, Mistral, and other open-source LLMs.
Build advanced RAG solutions using vector databases, embeddings, semantic search, reranking techniques, and hybrid search architectures.
Develop prompt engineering strategies, evaluation frameworks, guardrails, hallucination mitigation techniques, and LLM optimization approaches such as LoRA, QLoRA, PEFT, and reinforcement learning.
Integrate LLMs with enterprise applications, APIs, databases, internal tools, and business workflows.
Design and implement autonomous and semi-autonomous AI agents with reasoning, planning, memory management, tool usage, and workflow orchestration capabilities.
Build multi-agent systems using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, MCP, A2A, or similar technologies.
Develop agent collaboration patterns, task decomposition mechanisms, monitoring, observability, governance, and evaluation frameworks.
Build and maintain end-to-end ML pipelines, including data ingestion, feature engineering, model training, evaluation, deployment, monitoring, model versioning, experiment tracking, and automated retraining.
Develop predictive models, recommendation systems, anomaly detection solutions, NLP applications, and deep learning models.
Design CI/CD pipelines for machine learning, Generative AI, and AI-powered applications.
Deploy and manage AI workloads using Docker, Kubernetes, and cloud-native services.
Monitor model drift, performance degradation, latency, data quality, and production reliability.
Build scalable inference services and APIs for real-time and batch processing workloads.
Architect cloud-native AI solutions on AWS, Azure, or Google Cloud Platform.
Use managed AI services such as AWS SageMaker, AWS Bedrock, Azure OpenAI, Azure ML, Vertex AI, and similar platforms.
Design scalable data and AI architectures using distributed computing platforms such as Databricks, Spark, Ray, and BigQuery.
Optimize cloud infrastructure for performance, security, scalability, reliability, and operational cost.
Establish AI governance, model lifecycle management, responsible AI practices, secure AI development standards, auditability, and compliance controls.
Collaborate with data scientists, software engineers, data engineers, product managers, and business stakeholders to deliver production AI solutions.

Requirements

Upper C1 or higher English proficiency, both written and verbal.
7+ years of experience in software engineering, machine learning, artificial intelligence, or related technical roles.
Minimum 3+ years of hands-on experience developing and deploying Generative AI applications in production environments.
Strong Python programming skills with experience building scalable backend systems and production-grade AI applications.
Extensive experience with PyTorch, TensorFlow, scikit-learn, and Hugging Face.
Proven experience developing RAG architectures, knowledge retrieval systems, Agentic AI solutions, and multi-agent workflows.
Strong understanding of vector databases, embeddings, semantic search, hybrid search, reranking, and retrieval optimization.
Experience integrating LLMs with APIs, databases, enterprise applications, and business workflows.
Strong understanding of MLOps, CI/CD, containerization, model deployment, monitoring, and model lifecycle management.
Hands-on experience with Docker, Kubernetes, and cloud-native deployment patterns.
Experience with cloud platforms including AWS, Azure, or Google Cloud Platform.
Experience with managed AI services such as AWS SageMaker, AWS Bedrock, Azure OpenAI, Azure ML, or Google Vertex AI.
Experience with distributed computing and data platforms such as Databricks, Apache Spark, Ray, BigQuery, or similar technologies.

Preferred Qualifications

Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, Chroma, Qdrant, or similar technologies.
Experience with AI orchestration and agent frameworks such as LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, MCP, or A2A.
Experience with AI governance, responsible AI, model monitoring, auditability, compliance controls, LLM evaluation, hallucination detection, prompt testing, benchmarking, and production AI observability.
Experience with recommendation systems, personalization, forecasting, anomaly detection, NLP, deep learning, real-time inference, or batch inference systems.
Familiarity with DevOps practices, infrastructure-as-code, cloud automation tools, and agile development environments.

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