Quantum Applications and Data Engineer – Quantum AI/ML & Hybrid Computing

Role Summary We are seeking a Quantum Applications and Data Engineer to design, implement, and evaluate quantum computing applications, quantum AI/ML models, and hybrid quantum–classical workflows. The successful candidate will translate selected business and research problems into practical experiments, develop proof-of-concept solutions, and assess their performance against appropriate classical approaches. The role also includes dedicated data engineering responsibilities to ensure reliable data preparation, automated processing, and reproducible experimentation. This position requires strong Python programming skills, a foundation in machine learning and quantum computing, and the ability to communicate experimental findings clearly. Key Responsibilities 1. Quantum AI and Machine Learning Develop and evaluate quantum machine learning prototypes for selected classification, regression, and clustering use cases. Implement and assess quantum feature maps, quantum kernels, and parameterized quantum circuits. Explore variational quantum classifiers and hybrid quantum–classical learning architectures. Prepare suitable data-encoding strategies for quantum machine learning experiments. Integrate quantum model components with classical machine learning tools and workflows. Evaluate model performance using appropriate metrics, consistent datasets, and reproducible methods. Document training behavior, experimental results, resource requirements, and limitations. 2. Quantum Programming and Application Development Design, implement, test, and maintain quantum circuits using frameworks such as Qiskit or PennyLane. Develop reusable Python components for circuit construction, execution, and results analysis. Implement selected quantum algorithms within clearly defined proof-of-concept projects. Execute experiments using quantum simulators and, where available, cloud-accessible quantum hardware. Analyze the effects of circuit design, measurement strategies, and execution conditions. Maintain version-controlled source code, test cases, and technical documentation. 3. Hybrid Quantum–Classical Workflows Develop workflows connecting classical data processing, quantum computation, and results interpretation. Implement iterative optimization workflows involving parameterized circuits and classical optimizers. Integrate quantum experiments with Python-based AI/Libraries. Automate experiment configuration, execution, monitoring, and output collection. Develop prototypes for selected machine learning and optimization problems. Identify integration constraints and document technical trade-offs. 4. Quantum Experimentation, Benchmarking and Use-Case Assessment Assess business and research requirements to identify suitable quantum computing experiments. Define problem statements, assumptions, evaluation criteria, and experimental scope. Develop or select classical reference implementations for comparison. Benchmark experimental approaches using relevant measures, including predictive performance, execution time, and computational resources. Investigate the effects of noise, sampling variability, and hardware limitations where applicable. Review relevant technical literature and reproduce selected methods. Present findings through technical reports, demonstrations, and stakeholder discussions. Recommend whether to continue, revise, or discontinue an approach based on evidence, without presuming quantum advantage. 5. Data Engineering and Data Preparation Develop and maintain Python and SQL workflows to ingest, clean, transform, and validate datasets. Build lightweight ETL pipelines supporting quantum computing and AI/ML experiments. Prepare consistent training, validation, and test datasets for classical and quantum model comparisons. Implement checks for missing values, duplicates, inconsistent formats, and transformation errors. Manage dataset versions, experiment inputs, configurations, and outputs. Automate recurring data preparation, results aggregation, and analytical processing. Maintain data mappings, transformation rules, and processing documentation. Ensure data workflows support reproducibility and reliable experimental evaluation. Required Qualifications and Experience - Bachelor's degree in engineering, Computer Science, Mathematics, Physics, or a related discipline. - Relevant technical experience in software development, data engineering, analytics, or machine learning. - Formal quantum computing education or equivalent demonstrable knowledge supported by practical projects. - Strong Python programming skills and working knowledge of SQL. - Experience with data preparation, transformation, validation, and automation. - Understanding of machine learning fundamentals, model evaluation, and experimental design. - Foundational knowledge of quantum circuits, gates, measurement, superposition, and entanglement. - Working knowledge of linear algebra, probability, and optimization. Hands-on exposure to at least one quantum software framework through coursework, research, or project work. - Ability to analyze results critically and communicate technical findings clearly. Preferred Qualifications - Relevant advanced certificate or postgraduate study in quantum computing, quantum AI, or quantum machine learning, including programs such as the Advanced Certificate Programmer in Quantum Computing& Quantum AI and Machine Learning (QCML), Indian Institute of Technology Kanpur, or similar. - Project experience with quantum kernels, variational quantum algorithms, or quantum neural networks. - Familiarity with scikit-learn, PyTorch, or comparable machine learning libraries. - Exposure to simulator-based experimentation and quantum hardware execution. - Experience with Git, automated testing, and experiment tracking. - Familiarity with cloud platforms, relational databases, and ETL tools. - Experience developing technical demonstrations, solution proposals, or proof-of-concept presentations. Core Competencies - Analytical thinking and structured problem-solving. - Reproducible experimentation and evidence-based evaluation. - Software development and documentation discipline. - Ability to translate requirements into testable technical solutions. - Collaboration across engineering, research, and business teams. - Clear communication of uncertainty, limitations, and technical trade-offs. Expected Deliverables - Functional quantum computing and quantum AI/ML prototypes. - Reusable hybrid quantum–classical workflows. - Benchmark reports comparing experimental results with classical baselines. - Reliable data preparation pipelines and validated datasets. - Version-controlled code, experiment records, and technical documentation. - Technical demonstrations and recommendations on use-case feasibility. Performance Measures - Quality, correctness, and reproducibility of implemented solutions. - Rigour of experimental design and benchmarking. - Reliability and accuracy of supporting data workflows. - Clarity and completeness of technical documentation. - Delivery against agreed project milestones. - Effectiveness of collaboration and stakeholder communication. Role Scope The position maintains a defined allocation of 80% quantum computing-related responsibilities and 20% data engineering responsibilities. Data engineering is a mandatory component focused on enabling quantum and AI/ML development. General enterprise reporting, unrelated dashboard maintenance, and routine warehouse administration are outside the core scope.