Senior Machine Learning Engineer

Main tasks

• Lead the design, development, and deployment of advanced analytics components within industrial systems, with a focus on Generative AI, LLMs, computer vision, and signal processing.
• Build production-ready document intelligence pipelines to extract, validate, and structure data from semi-structured documents (e.g., invoices, statements, forms) using OCR + LLM workflows, including quality checks and exception handling.
• Develop LLM-powered “policy/rule reasoning” services that interpret complex

eligibility/contract/tariff-like rules and match them against site/customer profiles to

recommend optimal options and outcomes.

• Design and implement agentic workflows for scalable information discovery and data acquisition (e.g., automating collection of public/partner data sources), including tooling, safety constraints, and evaluation.
• Create orchestration, monitoring, and cost controls for GenAI systems (tracing,

latency/error monitoring, token usage/cost, standardized logging/alerts) to ensure

production reliability.

• Own end-to-end delivery: from feasibility studies and prototypes to deployment-ready services (APIs, batch jobs, workflows), collaborating with software engineers,

platform/MLOps, and domain experts.

• Establish evaluation methodology for LLM/agent systems (gold datasets, accuracy metrics, regression tests, offline/online evaluation, failure mode analysis) and drive iterative improvement.
• Mentor and lead technically, contributing to engineering standards, reusable templates, and knowledge sharing across teams.
• Contribute to innovation outputs (internal tech talks, invention disclosures/patents,

publications where relevant).

Requirements

• Master’s or PhD in Computer Science, Data Science, Mathematics, Engineering, or a related field.
• 7+ years building ML/AI systems in industrial, infrastructure, or enterprise settings, including taking solutions from prototype to production.
• Strong Python engineering skills and experience with ML/AI libraries and modern software practices (testing, packaging, APIs, CI/CD).
• Proven hands-on work with LLMs / Generative AI in applied settings, including prompt design, tool-using agents, and retrieval/grounding patterns (RAG).
• Senior Level - Experience building information extraction / document understanding solutions (OCR pipelines, structured outputs, validation, human-in-the-loop flows).
• Senior Level - Practical knowledge of workflow orchestration and production pipelines (e.g., Airflow-like orchestration, job scheduling, dependency management).
• Strong understanding of observability for AI systems (logging, tracing, evaluation, monitoring, and cost governance for LLM usage).
• Comfortable collaborating with stakeholders to translate ambiguous domain problems into measurable ML deliverables and robust services
• Experience in utilities / energy / building operations / infrastructure analytics, especially where cost optimization and rules-driven decisions matter. Prior work in time-series/signal processing or industrial monitoring, including anomaly detection and fault diagnostics.
• Computer vision experience (industrial or remote sensing), active learning, few-shot adaptation.
• Demonstrated innovation track record (patents / invention disclosures / technical publications).