AI/ML Engineer

DLYTICA  AI is seeking experienced Data Scientists to work on DataNature Sovereign AI Lakehouse, AI360 Studio, and enterprise Data and AI projects across North America, Nepal, and the Middle East.

Job Summary

• Location: Kathmandu, Nepal
• Work arrangement: Onsite
• Job type: Full-time
• Total openings: 10
• Experience:Minimum 3 to 15 years of professional experience after completing a bachelor’s degree.
• Senior/Lead opening: One position requiring 8+ years of experience

Key Responsibilities

• Build and productionise feature engineering, model training, and model-serving pipelines for machine learning and Generative AI use cases using Databricks and MLflow.
• Develop and deploy Generative AI and agentic systems, including Retrieval-Augmented Generation (RAG), vector search, LLM orchestration, and Databricks Genie Spaces.
• Collaborate with data scientists, ML scientists, and solution architects to transform experimental notebooks and AI prototypes into scalable, reliable, production-ready solutions.
• Develop reusable Databricks templates and accelerators for common AI/ML use cases, including RAG, agentic assistants, forecasting, and personalisation.
• Optimise Databricks jobs, clusters, and workflows for cost efficiency, performance, scalability, and reliability.
• Implement MLOps practices for experiment tracking, model management, deployment, evaluation, monitoring, and governance.
• Work directly with enterprise clients and cross-functional teams to gather requirements, troubleshoot technical issues, and deliver effective AI/ML solutions.
• Prepare technical documentation, deployment guides, and operational runbooks for production systems.
• Apply machine learning and data science techniques to enterprise use cases, including recommendation systems, ranking, forecasting, and personalisation.

Required Skills

• Minimum 3 years of relevant industry experience as an AI/ML Engineer, Machine Learning Engineer, or in a similar role, with hands-on experience using Databricks.
• Strong programming skills in Python and PySpark, including the ability to develop production-quality Spark jobs and pipelines.
• Practical experience delivering Generative AI and agentic systems in production, including RAG architectures and LLM orchestration frameworks such as LangChain or LangGraph.
• Hands-on experience with machine learning and data science libraries such as pandas, scikit-learn, and PyTorch.
• Experience with MLflow or equivalent tools for experiment tracking, model registry, and model serving.
• Strong SQL skills and experience with cloud data platforms such as Azure, AWS, or GCP. Azure Databricks experience is preferred.
• Understanding of MLOps and AgentOps practices, including model evaluation, monitoring, governance, and lifecycle management.
• Experience building or supporting enterprise-scale ML systems, such as recommendation engines, ranking systems, forecasting, or personalisation solutions.
• Knowledge of model deployment, production monitoring, and performance optimisation.
• Strong analytical, problem-solving, and debugging skills.

Additional Requirements

• Strong professional English communication skills.
• Ability to participate confidently in client-facing discussions and technical meetings.
• Strong analytical thinking and problem-solving abilities.
• Ability to communicate technical findings, recommendations, and solutions effectively to both technical and non-technical stakeholders.
• Ability to collaborate with cross-functional teams and translate business requirements into practical technical solutions.
• Willingness to work directly with enterprise clients in a consulting-oriented environment.

Nice to Have

• Databricks certifications, such as Machine Learning Associate/Professional or Generative AI Engineer Associate.
• Practical experience with Delta Lake and lakehouse/medallion architecture patterns.
• Working knowledge of Unity Catalog or equivalent data governance and access-control tools.
• Previous experience in AI/ML consulting or professional services.
• Familiarity with experimentation and A/B testing pipelines built on Databricks.
• Exposure to causal inference and applied statistics, including uplift modelling and Double Machine Learning (Double ML).