Senior 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 a company where high-growth startups turn when they need to move faster, scale smarter, and make the most of the cloud. As an AWS Premier Partner and Strategic Partner, the company delivers hands-on DevOps, FinOps, and GenAI support that drives real results. The company works across EMEA, fueling innovation and solving complex challenges daily.
About the role:
We’re hiring a Senior MLOps Engineer to join the team. You will work hands-on with innovative startups’ Data Science and R&D teams and be in charge of various projects. You will develop robust MLOps pipelines, create cutting-edge Generative AI solutions, implement the latest technologies, and ensure the customers’ success by overseeing the project from inception to completion.
Requirements:
– 3+ years of experience in AI/ML/MLOps engineering with a track record of building and shipping production-grade AI systems.
– 5+ years of hands-on experience with Python and deep learning frameworks such as PyTorch and HuggingFace Transformers.
– Experience with orchestration frameworks such as LangChain, LangGraph, Strands Agents, CrewAI, and Bedrock Agents.
– Solid understanding of embedding strategies, retrieval optimization, and semantic search using vector stores.
– Experience with AWS services, including Bedrock, SageMaker, Lambda, and other serverless and container-based deployment approaches.
– Familiarity with MLOps practices, including CI/CD for models, monitoring, and reproducibility in cloud-native environments.
– Strong communication skills and the ability to collaborate across product and engineering teams.
– Fluent written and verbal communication skills in English.
– 5+ years of hands-on experience with Python and deep learning frameworks such as PyTorch and HuggingFace Transformers.
– Experience with orchestration frameworks such as LangChain, LangGraph, Strands Agents, CrewAI, and Bedrock Agents.
– Solid understanding of embedding strategies, retrieval optimization, and semantic search using vector stores.
– Experience with AWS services, including Bedrock, SageMaker, Lambda, and other serverless and container-based deployment approaches.
– Familiarity with MLOps practices, including CI/CD for models, monitoring, and reproducibility in cloud-native environments.
– Strong communication skills and the ability to collaborate across product and engineering teams.
– Fluent written and verbal communication skills in English.
Responsibilities:
– Design, build, and deploy GenAI systems using LLMs for real-world applications, including agentic workflows, RAG pipelines, and orchestration logic.
– Implement multi-step reasoning systems using frameworks like LangChain, LangGraph, Strands Agents, CrewAI, and Bedrock Agents.
– Optimize retrieval pipelines with efficient embedding strategies and vector stores such as OpenSearch, PostgreSQL pgvector, Pinecone, and others.
– Rapidly experiment with and integrate new research and GenAI tooling into scalable prototypes and customer-ready solutions.
– Collaborate with cross-functional teams to ship AI-powered features and assist in aligning AI efforts with business goals.
– Implement multi-step reasoning systems using frameworks like LangChain, LangGraph, Strands Agents, CrewAI, and Bedrock Agents.
– Optimize retrieval pipelines with efficient embedding strategies and vector stores such as OpenSearch, PostgreSQL pgvector, Pinecone, and others.
– Rapidly experiment with and integrate new research and GenAI tooling into scalable prototypes and customer-ready solutions.
– Collaborate with cross-functional teams to ship AI-powered features and assist in aligning AI efforts with business goals.
The company offers:
– Professional training and certifications covered by the company (AWS, FinOps, Kubernetes, etc.).
– International work environment.
– Referral program – enjoy cooperation with your colleagues and get a bonus.
– Company events and social gatherings (happy hours, team events, knowledge sharing, etc.).
– English classes.
– Soft skills training.
– International work environment.
– Referral program – enjoy cooperation with your colleagues and get a bonus.
– Company events and social gatherings (happy hours, team events, knowledge sharing, etc.).
– English classes.
– Soft skills training.
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