Algoleap - Senior Data Scientist - AI/ML

About the job:

Job Description:

Senior Data Scientist (AI, GenAI & MLOps) Location: Remote / Hybrid / On-site Employment Type: Full-Time Job Overview We are looking for a creative and driven Senior Data Scientist with 6+ years of professional experience across AI, Machine Learning, Data Science, and Automation. In this role, you will design, build, and deploy intelligent AI solutions to solve complex business problems. You will leverage modern AI paradigmsincluding Generative AI (LLMs, RAG) and Agentic AI principlesalongside foundational cloud technologies and MLOps practices to build scalable, automated, and impactful applications.

Key Responsibilities :

AI Solution Development & Deployment:

- Design, build, test, and deploy end-to-end Machine Learning, AI, and automation solutions tailored to business requirements.

- Generative & Agentic AI Solutions: Implement advanced AI architectures using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic workflows to automate decision-making and enhance user experiences.

- Data Pipeline & MLOps Integration: Collaborate with data and engineering teams to establish data pipelines and apply basic MLOps practices for model deployment, monitoring, and lifecycle management.

- Cloud Infrastructure & AI Services: Deploy and scale ML models on major cloud platforms (Azure, GCP, or AWS) utilizing native AI/ML services and CI/CD pipelines.

- Cross-Functional Collaboration: Partner with software engineers, product managers, and business stakeholders to translate domain requirements into effective technical AI strategies.

- Continuous Innovation: Stay up to date with emerging AI technologies, methodologies, and frameworks to keep our technology stack innovative and competitive.

Required Qualifications & Skills Experience:

- 6+ years of professional experience in Data Science, Machine Learning, Artificial Intelligence, or Intelligent Automation roles. Core Programming: Advanced proficiency in Python and its scientific stack (e.g., NumPy, Pandas, Scikit-Learn, PyTorch, TensorFlow).

- Generative AI & Agentic AI: Strong foundational understanding and hands-on experience with: Large Language Models (LLMs) and Prompt Engineering. Retrieval-Augmented Generation (RAG) architectures and Vector Databases. Agentic AI frameworks and multi-agent workflow automation principles.

- Cloud & Deployment: Experience with cloud-based AI/ML platforms (such as Azure Machine Learning, AWS SageMaker, or GCP Vertex AI) and a practical understanding of model deployment processes (REST APIs, microservices, containerization).

Data & MLOps Fundamentals: Working knowledge of data pipeline orchestration and core MLOps concepts (model tracking, versioning, deployment, and performance monitoring).

- Soft Skills & Core Competencies Problem-Solving: Analytical mindset with strong troubleshooting capabilities and a keen interest in learning and adopting new technologies.

- Communication: Ability to clearly explain complex technical concepts and AI models to non-technical business stakeholders. Teamwork: Collaborative attitude with a proven track record of working effectively in agile, cross-functional teams.