This role focuses on building production-grade AI systems that enable intelligent cost estimation, optimization, analysis, and decision support. This includes building end-to-end AI pipelines, LLM-powered applications, and AI-assisted workflows integrated into enterprise platforms. Working at the intersection of cost engineering, data platforms, and modern AI, you will partner closely with cost engineers, data engineers, and business stakeholders in a hands-on technical capacity.

1. AI/ML Solution Development & MLOps

• Design, develop, and deploy scalable AI/ML models tailored for cost engineering.
• Build and maintain end-to-end ML pipelines (data ingestion, feature engineering, model training, validation, deployment).
• Implement model monitoring, automated retraining, and continuous performance optimization in production environments.
• Apply robust MLOps practices for versioning, testing, monitoring, lifecycle management, and CI/CD pipelines.

• Design, develop, and deploy production-ready LLM-powered applications (copilots, chatbots, knowledge assistants) for cost engineering use cases.
• Implement RAG architectures, vector databases, and multi-agent orchestration utilizing enterprise data sources.

• Collaborate with data engineers to integrate AI solutions into enterprise data platforms (Databricks, Snowflake, Palantir Foundry, Azure, AWS, ServiceNow, Power Platform).
• Work across structured and unstructured data assets (BOMs, supplier data, costing data, engineering documents).
• Integrate AI capabilities directly into cost engineering tools, executive dashboards, and operational business workflows.

• Collaborate with onshore/offshore cross-functional teams and translate domain problems into actionable AI/ML solutions.
• Partner with business stakeholders to define requirements, success metrics, and KPIs.
• Provide technical leadership, mentor junior engineers, and promote AI capability building.
• Ensure AI solutions comply with enterprise data governance, security, traceability, and responsible AI standards.

• Education: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related quantitative field.
• Experience: 2+ years of hands-on experience in AI/ML engineering or applied data science roles.
• Core Skills: Strong proficiency in Python, SQL, and Git.
• GenAI / LLMs: Practical experience with LLMs, prompt engineering, RAG architecture, vector databases, and multi-agent systems/copilots (e.g., Copilot Studio).
• Data Platforms & ML Frameworks: Hands-on machine learning model development and experience with cloud/enterprise data platforms (Databricks, Snowflake, Palantir Foundry, Azure, AWS, etc.).
• MLOps / GenAIOps: Familiarity with MLOps tools (MLflow, Azure ML, CI/CD pipelines) and GenAIOps practices.
• Soft Skills: Strong communication, stakeholder management skills, and professional English proficiency.

• Industry experience in manufacturing, automotive, or cost engineering domains.
• Exposure to Power Platform tools (Power BI Fabric, Power Apps, Power Automate).
• Exposure to Robotic Process Automation (RPA) tools.