Senior AI Engineer - Nas Neuron

About the job
The AI Engineer is a forward-deployed, hands-on technical role focused on developing, deploying, and maintaining machine learning and AI solutions that deliver measurable business value across the organization. This position requires close engagement with business teams, senior stakeholders, analysts, IT, and operational functions to understand real business problems, shape solution options, and translate them into production-ready data and AI capabilities. This role encompasses problem definition through to production: building and training models, developing data pipelines, and deploying reliable, monitored services. The ideal candidate will have practical experience in machine learning, advanced analytics, and Generative AI, including Large Language Models (LLMs) and agent-based frameworks, combining engineering discipline with strong communication, structured problem-solving, and consulting-style delivery to create scalable solutions with clear business impact. Every engineer in the Strategy and Data team is expected to own use cases end-to-end, taking them from problem definition through to a deployed, adopted solution.

Responsibilities And Duties

• Develop, train, evaluate, and deploy machine learning models that address business problems across claims, network, operations, finance, and client functions.
• Build Generative AI and LLM solutions, including retrieval-augmented generation, document intelligence, and agent-based workflows using frameworks such as LangChain, LangGraph, CrewAI, and AG2.
• Design, build, and maintain scalable data pipelines to enable the movement, transformation, and synchronization of data between diverse sources, data warehouses, and cloud environments.
• Implement and optimize solutions for real-time and batch data ingestion, processing, and delivery in support of analytical and AI workloads.
• Deploy AI and machine learning solutions using API and container frameworks such as Flask, FastAPI, and Docker for scalable production environments.
• Apply established best practices for data modeling, ETL/ELT processes, data governance, and automation of data workflows.
• Develop and maintain analytics dashboards, reports, and analytical data products using Power BI, supporting the transition of reporting from legacy platforms.
• Work closely with data scientists, analysts, and business stakeholders to translate business challenges into data-driven solutions and communicate findings clearly to non-technical audiences.
• Operate as a forward-deployed engineer, working directly with business teams to frame problems, validate requirements, demonstrate prototypes, gather feedback, and iterate solutions toward adoption and measurable value.
• Support cloud-native data and AI architectures using platforms such as Snowflake, Microsoft Azure, and Docker, contributing to the build-out of the central analytical and AI environment.
• Monitor and troubleshoot model and data pipeline performance in production, proactively identifying areas for improvement and implementing solutions.
• Ensure compliance with data security, privacy, and AI governance policies throughout the model and data lifecycle.
• Take assigned use cases through each stage, from framing the problem with business stakeholders and preparing data, to building, evaluating, deploying the solution, and tracking its adoption and value, working within the team's architecture and engineering standards.

Knowledge, Skills And Experience

• 8+ years of hands-on experience in machine learning engineering, data science, or data engineering, with demonstrated experience deploying production-grade ML and AI solutions.
• Extensive experience with technical tools and platforms including Python, SQL, Git/GitHub, OpenAI/Azure OpenAI, Azure AI Foundry, Hugging Face, LangChain/LangGraph, MCP, Azure AI Search, Snowflake/Snowpark/Cortex, FastAPI, REST APIs, Docker, Kubernetes, Azure Functions, Azure Container Apps, Microsoft Azure, Entra ID, Azure Key Vault, and Power BI.
• Hands-on expertise with machine learning frameworks and libraries including Scikit-learn, XGBoost/LightGBM, PyTorch or TensorFlow, HuggingFace transformers, pandas, and NumPy.
• Practical experience building LLM-based solutions, including prompt engineering, retrieval-augmented generation, embeddings and vector stores, and agent frameworks.
• Proven ability to implement data movement, synchronization, and warehousing solutions across cloud and on-premises environments.
• Deep understanding of ETL/ELT, real-time and batch data processing, and data modeling best practices.
• Sound grounding in statistical methods, including regression, classification, time series forecasting, hypothesis testing, and A/B testing.
• Experience delivering within Agile/Scrum frameworks and model governance environments.
• Experience in healthcare, insurance, or third-party administration preferred, though not essential.
• Bachelor’s or master’s degree in computer science, Engineering, Mathematics, Statistics, or a related discipline.
• Relevant certifications in machine learning, artificial intelligence, cloud platforms, or data engineering preferred.
• Strong consulting-style communication skills, with the ability to structure ambiguous problems, facilitate stakeholder discussions, explain technical concepts clearly, and influence adoption across technical and non-technical audiences.
• Prior experience in management consulting, technology consulting, solution engineering, or client-facing delivery environments is strongly preferred.
• Exceptional analytical, problem-solving, and communication skills, with the ability to work both independently and collaboratively on high-performing teams.