Company Description Antariksha Labs is redefining education and innovation by delivering practical, hands-on learning experiences through modular hardware and a dynamic developer platform. The company focuses on satellite-inspired technologies, offering solutions that bridge the gap between theory and real-world applications, from model satellites for education to industrial-grade systems. We are an currently working at the intersection of energy systems, embedded hardware, and data-driven software.

Our work spans energy intelligence platforms built around India's smart metering transition — consumer, commercial, and utility-facing — alongside modular hardware and research platforms used across education, industry, and applied R&D.; We build systems that turn real measured data into decisions people act on, and we work directly with educators, industry partners, and engineering teams to take concepts through to deployable products.

Role Description The Electrical & Energy Algorithms Engineer will work part time in a remote capacity to design, implement, and optimise the algorithms that sit at the core of our energy intelligence platform — the layer that converts raw meter and sensor data into consumption forecasts, cost projections, tariff computations, and grid-level intelligence. This is an algorithm engineering role grounded in real electrical engineering. You will build tariff and time-of-day computation engines, consumption and load forecasting models, and optimisation logic for distributed energy resources and demand-side management.

You will model and simulate exhaustively against large volumes of interval data before anything reaches production, then validate every output against real measured ground truth. This is an opportunity that has the possibility to be converted to a full-time role with a fixed monthly salary, based on performance and business needs. Probation period pay will range from Rs.10,000 to Rs.14,000, that is to be performed remotely in India.

We are looking for someone who understands electricity and energy data, not just mathematics. Load curves, demand versus energy, power factor, tariff structure, DER behaviour, grid constraints — these need to be intuitive to you. The modelling and programming skills matter, but they are in service of genuine power systems understanding. You will work directly with engineering leadership and with the hardware, firmware, and software teams, and your algorithms will run across every product surface we ship.





Hands on exposure with DSPs, processors, sensors, and grid systems are expected.

Day-to-day Responsibilities

- Developing and testing tariff computation, forecasting, optimisation, and energy management algorithms
- Building the calculation engine that handles telescopic slabs, time-of-day windows, demand charges, duties, and surcharges — designed configuration-driven so current utilities and rate revisions are added as data, not code
- Modelling and simulating system behaviour against large interval datasets before implementation, including scenario sweeps and sensitivity analysis
- Developing consumption forecasting and cost projection models across household, facility, and utility-scale aggregates
- Building optimisation logic for load shifting, DER scheduling, battery dispatch, and demand response
- Owning the validation harness — every algorithm output checked against real bills and measured data
- Building the data quality layer: gap-filling, anomaly detection, meter health checks, and validation of field data
- Integrating algorithms into existing hardware and software frameworks, and exposing them as clean services to app and dashboard teams
- Collaborating with cross-functional teams to define requirements, validate performance, and refine models against experimental and field data
- Documenting algorithm architecture, assumptions, and interpretations so the system outlives any one engineer
- Supporting prototype deployment, performance analysis, and continuous improvement of data-driven solutions

Qualifications

- Bachelor's or Master's degree in Electrical & Electronics Engineering, Instrumentation & Control, Power Systems, Power Electronics, or Energy Engineering — or equivalent practical experience
- Strong grounding in power systems and electrical fundamentals: energy versus demand, kW/kVA/kVAh, power factor, load factor, maximum demand, load curve behaviour, single- and three-phase metering, CT/PT ratios, and net metering
- Tariff structure literacy — telescopic slabs, time-of-day regimes, demand charges, electricity duty,



fuel surcharge adjustments — and the ability to read a regulatory tariff order and turn it into correct computation
- Algorithm design and development for electrical and energy systems, with demonstrated ability to formulate a real-world constraint into a solvable problem
- Strong programming skills — Python (NumPy, pandas, SciPy) essential; C/C++ and MATLAB advantageous. Writing tested, modular, reviewable code rather than one-off scripts
- Modelling and simulation experience — MATLAB/Simulink, Simscape Electrical, Stateflow, PSCAD, ETAP, PowerFactory, or Python-based equivalents — including validating a model against measured ground truth
- Time-series and measured-data handling at scale: resampling, interpolation, clock drift, outlier detection, working with millions of interval records
- Forecasting methods — regression, ARIMA/SARIMAX, exponential smoothing, gradient boosting, walk-forward validation, weather and degree-day normalisation
- Optimisation methods — linear and mixed-integer programming, with exposure to PuLP, CVXPY, or scipy.optimize
- R&D; capability — modelling, simulation, and validation of electrical and energy systems
- Strong analytical, problem-solving, and documentation skills, with attention to detail and safety considerations. Ability to design and develop robust solutions using Algorithm Development, Algorithm Design, and Algorithms for electrical and energy systems.

Advantageous

- Distributed energy resources — rooftop solar, battery storage, EV charging behaviour, net metering economics, DER aggregation
- Grid and utility context — distribution network structure, feeder-level load behaviour, AT&C; losses, demand response and VPP concepts, smart metering rollout, regulatory tariff frameworks
- Signal processing — FFT, digital filtering, event detection, feature extraction from current and voltage waveforms
- Load disaggregation / NILM at project, thesis, or paper level
- Embedded systems and hardware-in-the-loop testing — algorithms running on constrained edge devices

What You'll Gain

- Architectural ownership of the computational core, carrying your design decisions for years
- Work spanning consumer, commercial, and utility scale, plus in-house metering hardware
- Deep domain expertise in the world's largest smart meter rollout and in DER integration
- Direct collaboration with engineering leadership across hardware, firmware, and software
- A clear path to owning the intelligence layer as the platform scales

📌 Electrical & Energy Algorithms Engineer (India)
🏢 Antariksha Labs
📍 India