Resume
Pranav Sharma
Senior AI Engineer | Forecasting, Pricing Analytics, and Decision Intelligence
Heilbronn, Germany
Email: pranavsharma619@gmail.com · LinkedIn: linkedin.com/in/topranav
I help teams make forecasting, pricing, and ML outputs more trustworthy, explainable, and usable in real decisions.
My work sits mainly across demand forecasting, pricing analytics, probabilistic ML, decision support, and AI Operations. I focus on systems that can be evaluated, explained, deployed, and trusted in real operational workflows.
Selected outcomes include forecasting workflow automation with approximately 100 person-hours/month reduction potential during scaled rollout, pricing analysis automation of approximately 40 hours/month, quantile and conformal uncertainty validation, Kubernetes-aligned deployment support, and lecturing AI Operations / AIOps at Hochschule Heilbronn.
Forecasting Sandbox · Pricing Decision Lite · Decision Kernel Lite · Blog · Contact
Profile
Senior AI Engineer focused on operational ML systems for forecasting, pricing analytics, decision intelligence, and MLOps.
Core strengths include:
- demand forecasting systems
- rolling backtesting and model benchmarking
- probabilistic forecasting with quantiles
- conformal evaluation of prediction intervals
- bias-aware evaluation and model governance
- pricing recommendation and optimization workflows
- decision intelligence and risk-aware decision support
- reproducible ML artifacts and deployment support
- AI Operations / AIOps teaching and applied MLOps mentoring
- explaining ML systems to technical and business stakeholders
The common thread in my work is simple:
turn uncertain model outputs into decisions that can be tested, explained, and improved.
Professional Experience
Senior AI Engineer — ebm-papst, Heilbronn
03/2024 – Present
Demand Forecasting: Baselines → ML → Probabilistic Forecasting
- Built end-to-end demand forecasting workflows for replenishment and safety-stock planning using statistical baselines, ML models, and probabilistic outputs.
- Standardized model evaluation across methods and horizons using rolling backtesting, MAE, bias, and composite scoring.
- Developed baseline control layers to compare simple statistical models against ML and foundation-model approaches.
- Implemented quantile forecasting workflows and calibration checks for uncertainty-aware planning.
- Evaluated conformal prediction intervals using coverage and interval-width tradeoffs.
- Benchmarked classical forecasting methods, ML models, and time-series foundation models through structured comparison.
- Created explainable model-selection logic to support operational trust and stakeholder adoption.
- Estimated forecasting workflow automation potential of approximately 100 person-hours/month during scaled rollout.
B2B Pricing Recommendation and Optimization
- Delivered ML-based pricing recommendation workflows for negotiation start prices.
- Automated pricing analysis workflows, reducing manual effort by approximately 40 hours/month.
- Built demand-response models and optimization logic under explicit business guardrails.
- Validated pricing logic through offline backtesting against baseline policy.
- Enabled what-if sensitivity analysis for commercial decision support.
- Supported interpretable driver analysis to improve stakeholder trust in recommendations.
- Supported Kubernetes-aligned deployment and reproducible ML artifact workflows.
Lecturer — Hochschule Heilbronn, Heilbronn
AI Operations / AIOps · M.Sc. Business Informatics
2026 – Present
- Teach AI Operations / AIOps for Master’s students in Business Informatics, focused on taking machine learning beyond notebooks.
- Cover practical MLOps foundations including reproducibility, experiment tracking, model packaging, deployment, serving, monitoring, and system reliability.
- Guide students through applied ML workflows: problem framing, dataset selection, baseline modeling, model comparison, inference design, API serving, and deployment readiness.
- Emphasize production-oriented thinking: evaluation discipline, reproducible artifacts, failure handling, documentation, and stakeholder-facing explanation.
- Mentor student teams on building AI systems that can be tested, explained, and presented as operational workflows.
Research Associate — Hahn-Schickard, Villingen-Schwenningen
08/2018 – 02/2025
- Developed deep-learning models for hyperspectral imaging, edge AI, sensor analytics, and resource-constrained ML systems.
- Achieved 91% classification accuracy in hyperspectral skin lesion analysis using transfer learning.
- Contributed to follow-on research funding of more than €1.5M.
- Led development of multi-class deep-learning classifiers for hyperspectral datasets in collaboration with research partners.
- Designed a reinforcement learning agent to improve Kubernetes cluster throughput and operational efficiency.
- Implemented AI methods including clustering, federated learning, explainable AI, and deep learning for imaging and industrial analysis.
- Managed project resources to enable a cost-neutral 6-month project extension despite price increases.
- Supervised and mentored 3 Master’s students.
Assistant Systems Engineer — Tata Consultancy Services, Noida, India
10/2013 – 04/2015
- Supported production maintenance for billing operations software.
- Resolved application issues through structured diagnosis, SQL analysis, and backend investigation.
- Developed frontend components in Qt/C++ and extended backend functionality in SQL.
- Contributed to stable production support in an enterprise application environment.
Selected Portfolio Systems
Forecasting Sandbox Lite
Transparent benchmarking and regime-aware model selection for time-series forecasting.
Focus: rolling backtesting, baseline-vs-ML comparison, MAE and bias-aware scoring, demand regimes, robustness analysis, and audit-ready model selection logic.
Pricing Decision Lite
Scenario-based pricing analytics under uncertainty.
Focus: price-response modeling, guardrails, what-if sensitivity, robust recommendation logic, and decision comparison under uncertainty.
Decision Kernel Lite
Structured decision analysis for comparing actions under uncertainty.
Focus: Expected Loss, Minimax Regret, CVaR, scenario matrices, and risk-aware decision comparison.
Education
M.Sc. Mechatronics Engineering — Hochschule Ravensburg-Weingarten
10/2015 – 03/2018 · Grade: 1.8
- Relevant coursework: Artificial Intelligence, Robot Learning, Robotics
- Master’s thesis: Automatic Maneuver Selection Using Reinforcement Learning, IAV GmbH · Grade: 1.2
- Internship: Automated Markov-based decision-making system at IAV GmbH
B.Tech. Electronics and Instrumentation Engineering — SRM University
07/2009 – 06/2013 · Grade: 7.136
- Relevant coursework: Artificial Intelligence and Expert Systems, Industrial Automation, Engineering Economics and Management
- Internship: Attitude Determination and Control Subsystems, SRM Nanosatellite Lab
Publications
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Srivastava, A., Sharma, P., Sikora, A., Bittner, A., Dehé, A. Data-driven Modelling of an Indirect Photoacoustic Carbon Dioxide Sensor, IEEE APSCON, 2024. DOI: 10.1109/APSCON60364.2024.10465802
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Srivastava, A., Sharma, P., Sikora, A., Bittner, A., Dehé, A. Temporal Behavior Analysis for the Impact of Combined Temperature and Humidity Variations on a Photoacoustic CO₂ Sensor, IEEE APSCON, 2024. DOI: 10.1109/APSCON60364.2024.10465885
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Sharma, P., Rüb, M., Gaida, D., Lutz, H., Sikora, A. Deep Learning in Resource and Data Constrained Edge Computing Systems, Machine Learning for Cyber Physical Systems. DOI: 10.1007/978-3-662-62746-4_5
Awards and Honors
- Certificate of Commendation — SRM University and Indian Space Research Organization
- Founder’s Scholar — SRM University
Certifications
PMP · IBM Generative AI Engineering with LLMs · DeepLearning.AI Deep Learning Specialization · Udacity Computer Vision Expert Nanodegree · Google Advanced Data Analytics · Duke AI Product Management · DeepLearning.AI GANs
Skills
Core Domains
Demand forecasting · Pricing analytics · Decision intelligence · Probabilistic ML · AI Operations / MLOps · Model evaluation and governance
Forecasting and Uncertainty
Statistical baselines · ML forecasting · Quantile forecasting · Conformal prediction · Rolling-origin backtesting · Bias-aware evaluation · Model robustness analysis · Demand regime segmentation
Decision and Pricing Systems
Scenario analysis · Sensitivity analysis · Expected Loss · Minimax Regret · CVaR · Guardrailed recommendation logic · What-if analysis
Technical Stack
Python · SQL · pandas · NumPy · SciPy · scikit-learn · statsmodels · LightGBM · XGBoost · PyTorch · Docker · Kubernetes · MLflow · DVC · FastAPI · Streamlit · Hugging Face Spaces · Git/GitHub · Linux/CLI
Contact
For roles, technical collaboration, consulting discussions, or advisory conversations:
Email: pranavsharma619@gmail.com
LinkedIn: linkedin.com/in/topranav
Compliance Note
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