ML Engineer
griddynamics
Job Description
Essential functions
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Design, develop, and implement LLM-based applications, with a focus on AI Agents, conversational systems, and intelligent decision-making pipelines.
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Develop and optimize prompts and prompt orchestration frameworks tailored to specific use cases.
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Implement vector search and retrieval-augmented generation (RAG) pipelines to improve model output relevance.
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Collaborate with product and data teams to translate business requirements into scalable AI-driven solutions.
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Deploy, optimize, and monitor AI applications in cloud environments (Azure, GCP, or AWS).
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Stay updated with advancements in LLMs, NLP, and generative AI technologies, incorporating best practices into projects.
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Contribute to code reviews, technical discussions, and knowledge sharing within the team.
Qualifications
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Looking for minimum 3+ years of experience.
Strong programming skills in Python (object-oriented programming, scripting, APIs, and libraries). -
Proven experience in building LLM-based applications and/or AI Agents.
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Proficiency in prompt engineering and evaluation techniques.
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Hands-on expertise with vector search technologies (e.g., FAISS, Pinecone, Weaviate, or equivalent).
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Experience deploying applications on at least one major cloud provider (Azure, GCP, or AWS).
Would be a plus
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Background in Data Science or Machine Learning Engineering, with experience in NLP techniques.
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Prior experience with Microsoft Azure AI and Cognitive Services, including deployment and scaling.
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Familiarity with MLOps practices, CI/CD for machine learning models, and monitoring tools.
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Knowledge of transformer architectures and model fine-tuning.
We offer
- Opportunity to work on bleeding-edge projects
- Work with a highly motivated and dedicated team
- Competitive salary
- Flexible schedule
- Benefits package - medical insurance, sports
- Corporate social events
- Professional development opportunities
- Well-equipped office