Data Scientist / ML Engineer

Are you a skilled Machine Learning engineer with a passion for Computer vision, NLP or Generative AI? Do you have a knack for understanding both the technical intricacies and the business implications of data-driven solutions? If so, we have an exciting opportunity for you to join our team as Machine Learning Engineer., pgvector , and PostgreSQL. AI Agents: Hands-on experience building AI agents and multi-agent systems using frameworks such as LangChain , LangGraph , CrewAI , or similar orchestration frameworks. Must demonstrate the ability to design agent architectures, manage tool integration, and handle complex agent workflows. Programming: Proficiency in Python with a strong emphasis on writing clean, maintainable, production-quality code. Familiarity with software engineering best practices (testing, code review, documentation). Cloud: Practical experience with Google Cloud Platform (GCP) services for ML workloads (e.g., Vertex AI, Cloud Run, GCS, BigQuery, Compute Engine). DevOps & MLOps: Docker: Proficiency in containerization - building, managing, and deploying Docker images and containers. GitLab: Proficient GitLab skills for version control, merge request workflows, and repository management. API Development, Drive/Participate the ideation, development, and execution of POCs and AI related project Develop and implement machine learning models, algorithms, and data-driven solutions to address complex business problems Collaborate cross-functionally with engineering, product management, and other relevant teams to integrate data-driven functionalities into our products

Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related quantitative field. Experience: 5+ years of professional experience in machine learning engineering, AI development, or a closely related role. Machine Learning & Statistics: Solid understanding of classical ML algorithms (e.g., tree-based models, SVMs, clustering, ensemble methods), feature engineering, model evaluation metrics, and statistical methods (hypothesis testing, regression analysis, probability distributions). LLM Expertise: Demonstrated project experience with large language models, including: Prompt engineering and prompt management strategies; LLM application development (end-to-end); Fine-tuning of large language models; Retrieval-augmented generation (RAG) pipeline design and implementation; Practical experience with vector stores such as ChromaDB, Experience with FastAPI , including request validation, async handling, and integration with ML model serving. Broader software development experience expected. At least B2 level of English. Soft Skills: Excellent communication skills Strong work ethic and high personal accountability Ownership mentality - takes full responsibility for deliverables and outcomes Proactive, self-starting approach to identifying problems and driving project success without waiting for direction.