AI Engineer

• AI Feature Development: Design, build, and deploy production-grade AI capabilities into our SaaS product, utilizing RAG pipelines, autonomous agentic tools, and Model Context Protocol (MCP).
• Architectural Engineering: Architect and maintain highly scalable, reliable backend solutions using ASP.NET Core and F#, strictly adhering to functional programming principles and event-driven architectures.
• Vector & Data Management: Implement and optimize vector databases to ensure ultra-low latency, highly accurate semantic search and contextual data retrieval.
• Model Selection & Evaluation: Evaluate, benchmark, and integrate diverse LLMs and foundation models, matching their unique capabilities to specific product requirements for optimal cost, speed, and accuracy.
• Cloud & AI Infrastructure: Orchestrate and manage secure, scalable workloads within the Azure ecosystem, specifically leveraging Azure AI deployments.

Native AI Engineer

• Consult clients during presales to assess AI readiness, constraints, and success criteria, translating visions into actionable requirements
• Prepare and present presales deliverables including architecture diagrams, assumptions, risks, estimates, scalability considerations, and implementation roadmaps
• Define AI use cases and agentic scenarios based on client needs
• Select and justify LLMs per use case based on requirements, cost, latency, safety, capability
• Architect multi-agent frameworks with orchestrator agents and MCP-style protocols
• Design and implement single-agent/multi-agent AI systems with defined roles, tool access, memory, safety boundaries
• Build orchestration logic (routing, delegation, retries, fallback strategies, consensus, human-in-the-loop flows)
• Develop RAG pipelines (data ingestion, chunking, embeddings, vector databases, hybrid retrieval, relevance optimization)
• Implement learning & feedback loops for continuous agent improvement
• Design custom or adapted models (prompt-tuned agents, LoRA fine-tuning, domain-inherited models)

Principal AI Engineer

• Build AI-assisted tooling for engineering workflows
• Improve developer productivity and operational efficiency
• Design systems that reduce repetitive manual engineering work
• Develop AI-driven solutions for pipeline analysis, failure classification, flaky test detection, release validation, and more
• Improve reliability and visibility of engineering pipelines
• Build AI systems for troubleshooting assistance and operational workflows
• Design and implement engineering copilots and internal engineering assistants
• Deploy and maintain production-grade AI services

Junior AI Engineer

• Design, train, and optimize ASR and TTS models for production use
• Design and develop AI-powered assistants
• Fine-tune and optimize large-scale models for specific business needs
• Collaborate with DevOps team to deploy the solution
• Plan and manage ML projects, articulate methodologies, and communicate results and business impact to stakeholders and customers

AI Engineer, Speech

Machine Learning Engineer

• You'll build and operate the data pipelines that power our AI systems — streaming, near-real-time, and batch.
• You'll work with high-volume structured and unstructured data on platforms like Spark and Databricks, building the infrastructure that connects raw data to production models across personalization, fraud, and GenAI use cases.
• Build and maintain ML inference and feature engineering pipelines on Databricks
• Develop feature pipelines — transformations, aggregations, time-based features at scale using PySpark
• Deploy and manage models in production — versioning, registry, serving (MLflow)
• Build batch inference jobs that generate predictions at scale
• Integrate ML outputs with streaming infrastructure (Kafka) for downstream consumption
• Optimize SQL queries across multiple engines (Databricks SQL, PostgreSQL)
• Monitor data quality, pipeline reliability, and model serving health
• Collaborate with ML scientists to productionize their research

Mid-Senior ML Engineer

• Проєктування, розробка та підтримка ефективних і надійних ETL пайплайнів
• Розробка та покращення моделей ранжування, побудова рекомендаційних систем та вирішення інших ML/DS задач
• Розгортання моделей, написання продакшн-коду, підтримка мікросервісів та моніторинг їх роботи
• Тісна співпраця з командою DevOps, щоб інтегрувати MLOps CI/CD у продукт
• Дизайн та проведення A/B тестів для перевірки гіпотез, аналіз результатів та пошук інсайтів у даних
• Проактивний пошук точок росту продукту спільно з бізнес-стейкхолдерами

• Build and maintain ML pipelines for training, evaluation, and deployment using tools like Databricks, MLFlow, Airflow, DBT, Sagemaker, Tecton
• Support AI scientist creating reproducible, containerized model training environments (on-demand and scheduled), and manage compute at scale (e.g., spot/GPU autoscaling)
• Define and implement observability and alerting for ML systems (model drift, data quality, feature coverage, etc.)
• Design and scale data ingestion and feature transformation flows using batch (e.g., Spark/BigQuery) and streaming (Kafka or equivalent)
• Contribute to internal Python libraries and platform tooling that accelerate experimentation and deployment for all model teams
• Ensure ML services are modular, testable, and monitored from day one
• Exploration and productionization of LLM-based features (e.g., retrieval pipelines, prompt evaluation, model serving)

Senior Machine Learning Platform/Ops Engineer

• Design and implement scalable, secure, and cost‑efficient MLOps solutions leveraging AWS and Databricks.
• Automate ML deployment pipelines, reducing manual intervention and operational overhead.
• Collaborate closely with data scientists to ensure solutions align with established MLOps architecture, best practices, and platform standards.
• Integrate security controls and compliance requirements throughout the entire machine learning lifecycle.
• Own and manage incidents end‑to‑end, from root cause analysis to prevention of future occurrences.
• Contribute to software system architecture and the design of platform‑level components.
• Build and optimize ML training, retraining, and inference pipelines, ensuring reliability and scalability.
• Enhance observability with metrics, logging, tracing, and dashboards to ensure system visibility and performance.
• Drive best practices in infrastructure automation, CI/CD, and cloud resource management across ML teams.

See all jobs on JobTailor