• Lead the end-to-end development of advanced AI solutions, including agentic systems, RAG pipelines, and multi-agent workflows. • Design, build, and optimize LLM-powered autonomous agents, workflow orchestrations, and RAG frameworks. • Create integrations to connect AI agents with enterprise data systems, APIs, and services. Focus on explainable AI and responsible AI principles. • Develop and refine machine learning models and generative AI applications. • Architect end-to-end AI infrastructure, define guardrails, access controls, responsible-AI checks, and compliance protocols to ensure ethical, secure model deployment, model versioning, deploy and manage AI agents on cloud platforms using CI/CD pipelines, and infrastructure as code. • Implement monitoring, logging, and observability for agent interactions, monitor model drift, system latency, and token economics to maintain cost-per-use efficiency and system reliability. • Develop metrics for evaluating and monitoring AI performance. • Contribute to technical documentation, prototyping strategies, and recommendations for future scaling. • Guide multi-disciplinary teams of software engineers, data engineers, and data scientists, setting the technical playbook and best practices.

• Define technical strategy and lead architectural design across cloud, data, and AI/ML systems for end-to-end engagements, owning architecture decisions and driving solutions from research through production at scale • Architect and ship production-grade multi-agent agentic AI systems, including agent orchestration, tool use, memory, and inter-agent communication patterns • Design and build with Amazon Bedrock AgentCore and complementary AWS GenAI services to deploy, scale, and operate agentic workloads securely in production • Architect scalable cloud-native solutions with a strong bias toward AWS, including multi-cloud and hybrid strategies where needed (AWS primary, with Azure, GCP, Kubernetes as secondary) • Design data architectures including warehouses, data lakes, and pipelines for batch and streaming workloads (e.g., Snowflake, Redshift, BigQuery, Spark, Kafka) • Design AI/ML systems including model serving, MLOps pipelines, feature stores, and LLM-based applications (e.g., SageMaker, Bedrock, AgentCore, Vertex AI, MLflow, Hugging Face) • Build and evolve scalable ML platforms, pipelines, and infrastructure that support reliable, repeatable model development and deployment across teams • Define infrastructure as code, CI/CD, and DevOps standards across engagements (e.g., Terraform, CloudFormation, GitHub Actions) • Drive performance, scalability, cost, and reliability optimization across deployed systems • Ensure architecture meets security, governance, and compliance requirements (e.g., GDPR, HIPAA, SOC2) • Lead cloud migrations and platform modernization initiatives • Set the standard for AI-forward engineering, using tools like Claude and Cursor with sophistication and helping the team adopt them effectively

• You'll work with the CEO and with teams across the company to find the highest-leverage work. • This role owns the cross-cutting investments no single team will prioritize, and builds for the teams that can't build for themselves. • A meaningful part of the role is spent with the people doing the work, engineers included, in enough depth to understand where their time actually goes. • From there you scope it, build it, secure it, roll it out, and keep it running. • You own the applications built on top of it, in partnership with Security from design time forward.

• Develop methods for analyzing electrochemical measurement data and other operational data • Implement algorithms for signal preprocessing, feature extraction, and reliable analysis of complex measurement data • Develop modern analysis algorithms as well as machine learning and AI models for condition assessment, fault diagnosis, and performance analysis • Validate developed models using laboratory and field data and continuously improve existing methods • Develop production-ready software modules and integrate analysis functions into our software platform • Work closely with software development, hardware development, and applications teams