MLOps Engineer
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.
About the client:
Our client is an IT company that develops technological solutions and products to help companies reach their full potential and meet the needs of their users. The team comprises over 600 specialists in IT and Digital, with solid expertise in various technology stacks necessary for creating complex solutions.
About the role:
We are looking for an MLOps Engineer specializing in Large Language Model (LLM) infrastructure to design and maintain the robust platform on which the AI models are developed, deployed, and monitored. As an MLOps Engineer, you will build the backbone of the machine learning operations – from scalable training pipelines to
Reliable deployment systems – ensuring that the NLP models (including LLMs) can be trained on large datasets and served to end-users efficiently.
This role sits at the intersection of software engineering, DevOps, and machine learning, and is crucial for accelerating the R&D in the Ukrainian LLM project. You’ll work closely with data scientists and software engineers to implement best-in-class infrastructure and workflows for the continuous delivery of AI innovations.
Requirements:
– Experience & Background: 4+ years of experience in DevOps, MLOps, or ML Infrastructure roles. Strong foundation in software engineering and DevOps principles as they apply to machine learning. A bachelor’s or Master’s in Computer Science, Engineering, or a related field is preferred.
– Cloud & Infrastructure: Extensive experience with cloud platforms (AWS, GCP, or Azure) and designing cloud-native applications for ML. Comfortable using cloud services for compute (EC2, GCP Compute, Azure VMs), storage (S3, Cloud Storage), container registry, and serverless components where appropriate. Experience managing infrastructure with Infrastructure-as-Code tools like Terraform or CloudFormation.
– Containerization & Orchestration: Proficiency in container technologies (Docker) and orchestration with Kubernetes. Ability to deploy, scale, and manage complex applications on Kubernetes clusters; experience with tools like Helm for Kubernetes package management. Knowledge of container security and networking basics in distributed systems.
– CI/CD & Automation: Strong experience implementing CI/CD pipelines for ML projects. Familiar with tools like Jenkins, GitLab CI, or GitHub Actions for automating testing and deployment of ML code and models. Experience with specialized ML CI/CD (e.g., TensorFlow Extended TFX, MLflow for model deployment) and GitOps workflows (Argo CD) is a plus.
– Programming & Scripting: Strong coding skills in Python, with experience in writing pipelines or automation scripts related to ML tasks. Familiarity with shell scripting and one or more general-purpose languages (Go, Java, or C++) for infrastructure tooling. Ability to debug and optimize code for performance (both in data pipelines and model inference code).
– ML Pipeline Knowledge: Solid understanding of the machine learning lifecycle and tools. Experience building or maintaining ML pipelines, possibly using frameworks like Kubeflow, Airflow, or custom solutions. Knowledge of model serving frameworks (TensorFlow Serving, TorchServe, NVIDIA Triton, or custom Flask/FastAPI servers for ML).
– Monitoring & Reliability: Experience setting up monitoring for applications and models (using Prometheus, Grafana, CloudWatch, or similar) and implementing alerting for anomalies. Understanding of model performance metrics and how to track them in production (e.g., accuracy on a validation stream, response latency). Familiarity with concepts of A/B testing or canary deployments for model updates in production.
– Security & Compliance: Basic understanding of security best practices in ML deployments, including data encryption, access control, and dealing with sensitive data in compliance with regulations. Experience implementing authentication/authorization for model endpoints and ensuring infrastructure complies with organizational security policies.
– Team Collaboration: Excellent collaboration skills to work with cross-functional teams. Experience interacting with data scientists to translate model requirements into scalable infrastructure. Strong documentation habits for
outlining system designs, runbooks for operations, and lessons learned.
Nice to have:
– LLM/AI Domain Experience: Previous experience deploying or fine-tuning large language models or other large-scale deep learning models in production. Knowledge of specialized optimizations for LLMs (such as model parallelism, quantization techniques like 8-bit or 4-bit quantization, and use of libraries like DeepSpeed or Hugging Face Accelerate for efficient training) will be highly regarded.
– Distributed Computing: Experience with distributed computing frameworks such as Ray for scaling up model training across multiple nodes. Familiarity with big data processing (Spark, Hadoop) and streaming data (Kafka, Flink) to support feeding data into ML systems in real time.
– Data Engineering Tools: Some experience with data pipelines and ETL. Knowledge of tools like Apache Airflow, Kafka, or dbt, and how they integrate into ML pipelines. Understanding of data warehousing concepts (Snowflake,
BigQuery) and how processed data is used for model training.
– Versioning & Experiment Tracking: Experience with ML experiment tracking and model registry tools (e.g., MLflow, Weights & Biases, DVC). Ensuring that every model version and experiment is logged and reproducible for auditing and improvement cycles.
– Vector Databases & Retrieval: Familiarity with vector databases (Pinecone, Weaviate, FAISS) and retrieval systems used in conjunction with LLMs for augmented generation is a plus.
– High-Performance Computing: Exposure to HPC environments or on-prem GPU clusters for training large models. Understanding of how to maximize GPU utilization, manage job scheduling (with tools like Slurm or Kubernetes operators for ML), and profile model performance to remove bottlenecks.
– Continuous Learning: Up-to-date with the latest developments in MLOps and LLMOps (Large Model Ops). Active interest in new tools or frameworks in the MLOps ecosystem (e.g., model optimization libraries, new orchestration tools) and a drive to evaluate and introduce them to improve the processes.
Responsibilities:
– Design and implement modern, scalable ML infrastructure (cloud-native or on-premises) to support both experimentation and production deployment of NLP/LLM models. This includes setting up systems for distributed model training (leveraging GPUs or TPUs across multiple nodes) and high-throughput model serving (APIs, microservices).
– Develop end-to-end pipelines for model training, validation, and deployment.
– Automate the ML workflow from data ingestion and feature processing to model training and evaluation, using technologies like Docker and CI/CD pipelines to ensure reproducibility and reliability.
– Collaborate with Data Scientists and ML Engineers to design MLOps solutions that meet model performance and latency requirements.
– Architect deployment patterns (batch, real-time, streaming inference) appropriate for various use-cases (e.g., a real-time chatbot vs. offline analysis).
– Implement and uphold best practices in MLOps, including automated testing of ML code, continuous integration/continuous deployment for model updates, and rigorous version control for code, data, and model artifacts.
– Ensure every model and dataset is properly versioned and reproducible.
– Set up monitoring and alerting for deployed models and data pipelines.
– Use tools to track model performance (latency, throughput) and accuracy drift in production.
– Implement logging and observability frameworks to quickly detect anomalies or degradations in model outputs.
– Manage and optimize our Kubernetes-based deployment environments. Containerize ML services and use orchestration (Kubernetes, Docker Swarm, or similar) to scale model serving infrastructure.
– Handle cluster provisioning, health, and upgrades, possibly using Helm charts for managing LLM services.
– Maintain infrastructure-as-code (e.g., Terraform, Ansible) for provisioning cloud resources and ML infrastructure, enabling reproducible and auditable changes to the environment.
– Ensure the infrastructure is scalable, cost-effective, and secure.
– Perform code reviews and guide other engineers (both MLOps and ML developers) on building efficient and maintainable pipelines.
– Troubleshoot issues across the ML lifecycle, from data processing bottlenecks to model deployment failures, and continuously improve system robustness.
The company offers:
– Competitive salary.
– Equity options in a fast-growing AI company.
– Remote-friendly work culture.
– Opportunity to shape a product at the intersection of AI and human productivity.
– Work with a passionate, senior team building cutting-edge tech for real-world business use.
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