Machine learning engineer in agentic systems

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

• 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.

• 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.
• Будет плюсом: 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

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