Lead Machine Learning Engineer

• 4+ years of experience in Data Science, Machine Learning, AI Engineering, or related roles.
• Strong hands-on experience designing and delivering AI/ML solutions for enterprise use cases.
• Strong practical knowledge of Generative AI, LLMs, RAG, AI agents, NLP, and modern AI application architectures.
• Hands-on experience with prompt engineering, model evaluation, fine-tuning, and LLM application development.
• Advanced proficiency in Python and experience with frameworks such as PyTorch, TensorFlow, Hugging Face, or similar technologies.
• Strong understanding of the complete ML lifecycle, including data preparation, model development, evaluation, deployment, monitoring, and optimization.
• Experience with MLOps practices and production ML environments.
• Experience deploying AI workloads in on-premises and/or hybrid environments, with familiarity with Azure, AWS, or Google Cloud.
• Experience with containerized and Kubernetes-based deployments.
• Ability to work directly with enterprise customers and effectively communicate complex technical concepts.
• Strong analytical, problem-solving, and communication skills.
• Availability to travel up to 2–3 weeks per month, primarily to support customer and partner engagements.

Nice-to-have

• Hands-on experience with NVIDIA AI Enterprise, NVIDIA GPU infrastructure, CUDA, NIM, NeMo, or related NVIDIA AI technologies.
• Experience optimizing and deploying LLM inference workloads on GPU infrastructure.
• Experience with Kubernetes and/or VMware-based enterprise environments.
• Experience integrating AI solutions with platforms such as Snowflake, Databricks, Splunk, ServiceNow, IBM watsonx, or Cisco technologies.
• Proven experience successfully delivering AI solutions in one or more countries across the Middle East or Central Asia.
• Experience delivering solutions for higly regulated environments.
• Experience working on AI engagements involving technology partners and OEMs

About the role

We are looking for a Data Scientist / ML Engineer to design, develop, and deliver AI and machine learning solutions for enterprise client engagements, with a primary focus on projects across the Middle East and Central Asia.

In this role, you will work closely with Solution Architects, engineers, business stakeholders, and technology partners to design, build, validate, and deploy production-ready AI solutions addressing complex enterprise business challenges.

You will work extensively with Generative AI, LLMs, RAG, AI agents, and traditional machine learning, with a strong focus on NVIDIA-based AI solutions deployed primarily in on-premises or hybrid environments.

,[Design, develop, train, evaluate, and deploy machine learning and AI models for enterprise use cases., Build and optimize LLM, RAG, AI agent, NLP, and Generative AI applications., Perform data exploration, preprocessing, feature engineering, experimentation, and model evaluation., Develop production-ready ML pipelines, inference services, APIs, and AI application components., Apply prompt engineering, model optimization, fine-tuning, and evaluation techniques where appropriate., Optimize AI workloads for GPU-based infrastructure and enterprise deployment environments., Implement and maintain MLOps practices, including model versioning, deployment, monitoring, evaluation, and lifecycle management., Work closely with Solution Architects to translate solution designs into production-ready implementations., Collaborate with data engineers, software engineers, infrastructure specialists, and customer teams to integrate AI solutions with existing enterprise environments., Monitor and troubleshoot AI/ML solutions in production and continuously improve model quality, performance, reliability, and cost efficiency., Communicate technical findings, results, limitations, and recommendations to both technical and non-technical stakeholders., Contribute to reusable AI components, engineering standards, technical documentation, and best practices.] Requirements: Data science, Machine learning, AI, Use cases, NLP, Python, PyTorch, TensorFlow, MLOps, Azure, AWS, Google cloud, Kubernetes, Communication skills, GPU, CUDA, Snowflake, Databricks, Splunk, IBM, Cisco Additionally: Sport subscription, Training budget, Private healthcare, International projects, Flat structure, Small teams, Free coffee, Canteen, Bike parking, Free snacks, Free parking, In-house trainings, Modern office, No dress code.