Machine Learning Engineer (Senior Level)

Полная ставка

Ереван

Условия контракта Постоянный

Категория Программирование

Подать заявку

Описание работы

Acba bank is looking for a Senior Machine Learning Engineer to join our team

Обязанности

Design, develop, and deploy complex agentic workflows and automation ecosystems.
Securely expose internal systems as tools via MCP and engineer stateful multi-agent systems.
Optimize Time-To-First-Token and Tokens/sec for inference on in-house multi-GPU nodes.
Architect systems capable of handling thousands of concurrent requests.
Design fault-tolerant systems that gracefully handle the unpredictability of LLMs.
Spearhead the evaluation and testing of agentic workflows.
Mentor junior engineers through thoughtful code reviews and design feedback.
Maintain up-to-date knowledge of related MLOps and data science topics and technologies.
Build and optimize data pipelines, ETL processes, and model-serving frameworks
Model business requirements into structured software plans.

Требования

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
3+ years of experience building and operating scalable distributed and high-availability AI systems, with at least 2 projects shipped to production.
Demonstrated experience building and deploying agentic systems, chatbots, or intelligent automation workflows.
Exceptional proficiency in Python and FastAPI, strong understanding of OOP, software design patterns, and clean architecture.
Experience with containerization via Docker and Kubernetes.
Experience with agent orchestration frameworks like LangGraph and the MCP protocol.
Experience with RAG architectures, vector databases (FAISS, Qdrant, Milvus), and semantic retrieval systems.
Extensive experience working with SQL (T-SQL, Python’s SQLAlchemy toolkit) and NoSQL (e.g. Redis) databases.
Strong engineering rigor, including commitment to TDD, automated testing strategies for ML models, and building highly observable AI systems.
Evaluation-centric approach to building AI systems, deep understanding of classical and LLM metrics (precision/recall, BLEU, ROUGE, faithfulness, answer relevance, etc.).
Expertise in MLOps frameworks such as MLflow and Kubeflow.
Experience with on-premise LLM deployments is a plus.
Experience with PEFT techniques (LoRA, QLoRA, Prefix Tuning, FSDP) is a plus.
Proficiency in at least one other high-performance systems language (C, C++, Rust, Go) is a plus.
Open-source contributions and well-documented learning journeys are a plus.