FunctionEngineering<h1>Our Company</h1>We’re Hitachi Vantara, the data foundation trusted by the world’s innovators. Our resilient, high-performance data infrastructure means that customers – from banks to theme parks ­– can focus on achieving the incredible with data.   If you’ve seen the Las Vegas Sphere, you’ve seen just one example of how we empower businesses to automate, optimize, innovate – and wow their customers. Right now, we’re laying the foundation for our next wave of growth.  We’re looking for people who love being part of a diverse, global team – and who get excited about making a real-world impact with data.<h1>Job description</h1>Position OverviewWe are looking for an experienced Data Scientist / ML Engineer with deep expertise in applied machine learning and natural language processing (NLP), along with strong exposure to modern MLOps practices. The ideal candidate combines solid theoretical foundations with hands-on experience in building, deploying, and maintaining large-scale ML systems, including LLM-based applications, in cloud-native and Kubernetes-based environments.What You Will Do<ul><li>Translate complex business and product requirements into scalable AI/ML solutions using classical ML, deep learning, and GenAI techniques</li><li>Design, develop, fine-tune, and evaluate models for NLP tasks such as information extraction, classification, entity recognition, and semantic understanding</li><li>Build and productionize end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring</li><li>Implement LLM-based solutions using prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning approaches</li><li>Deploy and manage training and inference workloads on Kubernetes-based platforms (e.g., Kubeflow, KServe, Ray, or similar)</li><li>Develop scalable APIs and microservices for model inference with performance, latency, and cost considerations</li><li>Establish continuous training, evaluation, and feedback loops (CI/CD/CT pipelines) for model improvement</li><li>Monitor model performance, data drift, and system health in production, and implement automated retraining strategies</li><li>Collaborate closely with data engineering, platform, and product teams to ensure seamless integration into production systems</li><li>Ensure compliance with data privacy, security, and governance standards throughout the ML lifecycle</li></ul>What You Will Need<ul><li>Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or a related field, with 6–10 years of industry experience delivering ML solutions in production</li><li>Strong hands-on experience in applying statistical methods, classical ML, and deep learning to solve real-world problems</li><li>Proven experience building and deploying NLP systems, particularly for information extraction, classification, and sensitive data detection</li><li>Solid understanding of evaluation methodologies and metrics for NLP and ML systems (e.g., precision/recall, F1, ROC-AUC, BLEU, etc.)</li><li>Practical experience with LLMs, including prompt engineering, fine-tuning, embeddings, and retrieval-based systems</li><li>Strong understanding of data privacy regulations (e.g., GDPR, HIPAA) and secure ML practices</li><li>Experience working in cross-functional teams to deliver production-grade systems with continuous feedback and iteration</li><li>Strong problem-solving skills and ability to balance research with engineering pragmatism</li></ul>Technical SkillsCore ML &amp; NLP<ul><li>Proficiency in Python and ML ecosystem</li><li>Strong experience with ML/DL frameworks: PyTorch, TensorFlow, Scikit-learn</li><li>Experience with NLP libraries: spaCy, NLTK, Hugging Face Transformers</li><li>Familiarity with LLM tooling: LangChain, LlamaIndex, OpenAI APIs, vector databases (FAISS, Pinecone, Weaviate), Langgraph</li></ul>MLOps &amp; Productionization<ul><li>Hands-on experience with Kubernetes for ML workloads (training and inference)</li><li>Experience with Kubeflow, MLflow, KServe, Ray, Airflow, or similar orchestration tools</li><li>Strong understanding of CI/CD pipelines for ML (CI/CD/CT)</li><li>Experience with model serving frameworks (FastAPI, TorchServe, Triton, etc.)</li><li>Experience with experiment tracking, model versioning, and reproducibility</li></ul>Data Engineering &amp; Systems<ul><li>Experience with distributed data processing tools (e.g., Spark, Dask)</li><li>Familiarity with data pipelines and feature stores.</li></ul>Nice to Have<ul><li>Experience with RAG architectures, Knowledge-graphs and GraphRAG, and knowledge-grounded LLMs</li><li>Exposure to GPU/accelerator-based training and optimization</li><li>Familiarity with observability tools (Prometheus, Grafana) for ML systems</li><li>Experience with security, privacy-preserving ML, or federated learning</li></ul>#LI-SP7<h1>About us</h1>We’re a global team of innovators. Together, we harness engineering excellence and passion for insight to co-create meaningful solutions to complex challenges. We turn organizations into data-driven leaders that can a make positive impact on their industries and society. If you believe that innovation can inspire the future, this is the place to fulfil your purpose and achieve your potential.<h1>Fostering innovation through diverse perspectives</h1>Hitachi is a global company operating across a wide range of industries and regions. One of the things that sets Hitachi apart is the diversity of our business and people, which drives our innovation and growth.We are committed to building an inclusive culture based on mutual respect and merit-based systems. We believe that when people feel valued, heard, and safe to express themselves, they do their best work.<h1>How we look after you</h1>We help take care of your today and tomorrow with industry-leading benefits, support, and services that look after your holistic health and wellbeing. We’re also champions of life balance and offer flexible arrangements that work for you (role and location dependent). We’re always looking for new ways of working that bring out our best, which leads to unexpected ideas. So here, you’ll experience a sense of belonging, and discover autonomy, freedom, and ownership as you work alongside talented people you enjoy sharing knowledge with.We’re proud to say we’re an equal opportunity employer and welcome all applicants for employment without attention to race, colour, religion, sex, sexual orientation, gender identity, national origin, veteran, age, disability status or any other protected characteristic. Should you need reasonable accommodations during the recruitment process, please let us know so that we can do our best to set you up for success.

Hitachi Energy is a global technology leader that is advancing a sustainable energy future for all. We serve customers in the utility, industry and infrastructure sectors with innovative solutions and services across the value chain. Together with customers and partners, we pioneer technologies and enable the digital transformation required to accelerate the energy transition towards a carbon-neutral future. We are advancing the world&rsquo;s energy system to become more sustainable, flexible and secure whilst balancing social, environmental and economic value. Hitachi Energy has a proven track record and unparalleled installed base in more than 140 countries.

Applied AI/ML Engineer

FunctionEngineering<h1>Our Company</h1>We’re Hitachi Vantara, the data foundation trusted by the world’s innovators. Our resilient, high-performance data infrastructure means that customers – from banks to theme parks ­– can focus on achieving the incredible with data.   If you’ve seen the Las Vegas Sphere, you’ve seen just one example of how we empower businesses to automate, optimize, innovate – and wow their customers. Right now, we’re laying the foundation for our next wave of growth.  We’re looking for people who love being part of a diverse, global team – and who get excited about making a real-world impact with data.<h1>Job description</h1>Position OverviewWe are looking for an experienced Data Scientist / ML Engineer with deep expertise in applied machine learning and natural language processing (NLP), along with strong exposure to modern MLOps practices. The ideal candidate combines solid theoretical foundations with hands-on experience in building, deploying, and maintaining large-scale ML systems, including LLM-based applications, in cloud-native and Kubernetes-based environments.What You Will Do<ul><li>Translate complex business and product requirements into scalable AI/ML solutions using classical ML, deep learning, and GenAI techniques</li><li>Design, develop, fine-tune, and evaluate models for NLP tasks such as information extraction, classification, entity recognition, and semantic understanding</li><li>Build and productionize end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring</li><li>Implement LLM-based solutions using prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning approaches</li><li>Deploy and manage training and inference workloads on Kubernetes-based platforms (e.g., Kubeflow, KServe, Ray, or similar)</li><li>Develop scalable APIs and microservices for model inference with performance, latency, and cost considerations</li><li>Establish continuous training, evaluation, and feedback loops (CI/CD/CT pipelines) for model improvement</li><li>Monitor model performance, data drift, and system health in production, and implement automated retraining strategies</li><li>Collaborate closely with data engineering, platform, and product teams to ensure seamless integration into production systems</li><li>Ensure compliance with data privacy, security, and governance standards throughout the ML lifecycle</li></ul>What You Will Need<ul><li>Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or a related field, with 6–10 years of industry experience delivering ML solutions in production</li><li>Strong hands-on experience in applying statistical methods, classical ML, and deep learning to solve real-world problems</li><li>Proven experience building and deploying NLP systems, particularly for information extraction, classification, and sensitive data detection</li><li>Solid understanding of evaluation methodologies and metrics for NLP and ML systems (e.g., precision/recall, F1, ROC-AUC, BLEU, etc.)</li><li>Practical experience with LLMs, including prompt engineering, fine-tuning, embeddings, and retrieval-based systems</li><li>Strong understanding of data privacy regulations (e.g., GDPR, HIPAA) and secure ML practices</li><li>Experience working in cross-functional teams to deliver production-grade systems with continuous feedback and iteration</li><li>Strong problem-solving skills and ability to balance research with engineering pragmatism</li></ul>Technical SkillsCore ML &amp; NLP<ul><li>Proficiency in Python and ML ecosystem</li><li>Strong experience with ML/DL frameworks: PyTorch, TensorFlow, Scikit-learn</li><li>Experience with NLP libraries: spaCy, NLTK, Hugging Face Transformers</li><li>Familiarity with LLM tooling: LangChain, LlamaIndex, OpenAI APIs, vector databases (FAISS, Pinecone, Weaviate), Langgraph</li></ul>MLOps &amp; Productionization<ul><li>Hands-on experience with Kubernetes for ML workloads (training and inference)</li><li>Experience with Kubeflow, MLflow, KServe, Ray, Airflow, or similar orchestration tools</li><li>Strong understanding of CI/CD pipelines for ML (CI/CD/CT)</li><li>Experience with model serving frameworks (FastAPI, TorchServe, Triton, etc.)</li><li>Experience with experiment tracking, model versioning, and reproducibility</li></ul>Data Engineering &amp; Systems<ul><li>Experience with distributed data processing tools (e.g., Spark, Dask)</li><li>Familiarity with data pipelines and feature stores.</li></ul>Nice to Have<ul><li>Experience with RAG architectures, Knowledge-graphs and GraphRAG, and knowledge-grounded LLMs</li><li>Exposure to GPU/accelerator-based training and optimization</li><li>Familiarity with observability tools (Prometheus, Grafana) for ML systems</li><li>Experience with security, privacy-preserving ML, or federated learning</li></ul>#LI-SP7<h1>About us</h1>We’re a global team of innovators. Together, we harness engineering excellence and passion for insight to co-create meaningful solutions to complex challenges. We turn organizations into data-driven leaders that can a make positive impact on their industries and society. If you believe that innovation can inspire the future, this is the place to fulfil your purpose and achieve your potential.<h1>Fostering innovation through diverse perspectives</h1>Hitachi is a global company operating across a wide range of industries and regions. One of the things that sets Hitachi apart is the diversity of our business and people, which drives our innovation and growth.We are committed to building an inclusive culture based on mutual respect and merit-based systems. We believe that when people feel valued, heard, and safe to express themselves, they do their best work.<h1>How we look after you</h1>We help take care of your today and tomorrow with industry-leading benefits, support, and services that look after your holistic health and wellbeing. We’re also champions of life balance and offer flexible arrangements that work for you (role and location dependent). We’re always looking for new ways of working that bring out our best, which leads to unexpected ideas. So here, you’ll experience a sense of belonging, and discover autonomy, freedom, and ownership as you work alongside talented people you enjoy sharing knowledge with.We’re proud to say we’re an equal opportunity employer and welcome all applicants for employment without attention to race, colour, religion, sex, sexual orientation, gender identity, national origin, veteran, age, disability status or any other protected characteristic. Should you need reasonable accommodations during the recruitment process, please let us know so that we can do our best to set you up for success.

Applied AI/ML Engineer

About the Job

About the Company

HITACHI ENERGY

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