Job Description
About Orbitron AI
Orbitron AI is on a mission to turn large language model magic into production-grade agentic systems and workflows that solve real problems for the world’s largest enterprises. We’ve recently raised $10 million in seed funding and are scaling fast across Dubai, Bengaluru, and Saudi Arabia.
The Team Behind Orbitron AI
Founded by an exceptional leadership team of: Former EY Partners and Ex-CTOs and Heads of Engineering from successful Series B+ startups. The founding team brings together seasoned business, product and engineering leaders who have built and scaled large organizations and have led enterprise projects valued at over $1 billion.
Why Join Orbitron AI?
- Massive Impact, Real Ownership → Ship end-to-end features, own outcomes, and see your work power mission-critical systems.
- Battle-Tested Leadership → Learn and grow with seasoned leaders who bring deep industry expertise and zero ego.
- Cutting Edge Stack → Work with Gen AI frontier models, Java, Python, Go, Type Script, GCP & edge compute, Vector DBs, DAG engines.
Our Cultural DNA
- Hustle → Move with urgency, bias for action, celebrate shipping.
- Ownership & Commitment → Act like a founder — your decisions shape the company.
- Founder’s Mindset → Challenge ideas, not people. Seek truth, not consensus.
- Continuous Learning → No legacy thinking; we invest in upskilling and sponsor courses &
Why This Role
Airline crew rostering is one of the most complex real-world optimization challenges, thousands of
constraints, millions of possibilities, and zero room for error.
We’re not looking for someone to maintain spreadsheets or tune simple heuristics. We’re building autonomous decision-making systems that blend classical optimization, AI, and domain intelligence to generate schedules that are safe, efficient, and disruption resilient. You’ll be the technical owner of our optimization engine, modelling constraints, building solvers, running
large-scale experiments and ultimately shipping a system that an entire industry depends on.
Bonus: we’re a company where using AI tools to accelerate your own workflow is not just allowed; it’s expected.
What You Bring
1. Bachelor’s or master’s degree in Operations Research, Computer Science, Industrial Engineering, Applied Mathematics, or equivalent practical experience
2. 5+ years of experience building or operating large-scale optimization or scheduling systems
3. Strong expertise in one or more optimization techniques:
a. Mixed-Integer Programming (MIP)
b. Constraint Programming (CP)
c. Heuristics / Metaheuristics (GA, Tabu, Simulated Annealing, LNS, etc.)
d. Column generation or decomposition methods
4. Hands-on experience with optimization tools like Gurobi, CPLEX, OR-Tools, Mini Zinc, Pyomo,
or custom solvers
5. Ability to model complex rule systems (legalities, constraints, fatigue rules, preferences, unions,
etc.)
6. Proficiency in Python (bonus: experience in C++ for performance-critical components)
7. Familiarity with ML-assisted optimization is a plus (RL, heuristic learning, forecasting, or delay
modelling)
8. Experience designing experiments, tuning solvers, and interpreting model output.
9. Strong understanding of production engineering practices: CI/CD, version control, testing
frameworks, monitoring.
10. Excellent communication and collaboration skills; ability to thrive in a fast-paced, problem-
solving, startup environment.
What You’ll Do
1. Design, build, and optimize the core scheduling engine for airline crew and cabin rosters — from
constraint modelling to solver implementation.
2. Build scalable, parameterized formulations for pairing, rostering, bidding, fatigue compliance,
legality checks, and disruption recovery.
3. Develop and tune algorithms (MIP, CP, heuristics, or hybrids) capable of handling very large
problem instances.
4. Create automated pipelines to run “what-if” scenarios, simulations, and optimization
experiments.
5. Integrate real-world data sources — schedules, crew profiles, aircraft rotations, rule sets — into
the optimization engine.
6. Collaborate closely with product, domain experts, and engineering teams to define rules,
constraints, and objective functions.
7. Experiment quickly: prototype new formulations, prune search spaces, evaluate heuristics, and
benchmark solver performance.
8. Work with AI/ML engineers to blend optimization with predictive modelling, reinforcement
learning, or heuristic learning where beneficial.
9. Help define reliability, feasibility guarantees, and quality metrics for solver outputs.
10. Turn complex airline rules into simple, elegant models that consistently produce feasible and
high-quality schedules.
11. Contribute to building the next generation of autonomous operational decision systems used
inside enterprise environments.
Perks & Benefits
- Top‑Tier Compensation & stock options with substantial potential upside.
- Latest Mac Book Pros + 4 K monitors
- Paid AI tool & cloud credits to experiment freely
- Premium health, wellness, and learning budgets
- A culture celebrating every victory.
- Continuous learning and skill development opportunities.
Orbitron AI is on a mission to turn large language model magic into production-grade agentic systems and workflows that solve real problems for the world’s largest enterprises. We’ve recently raised $10 million in seed funding and are scaling fast across Dubai, Bengaluru, and Saudi Arabia.
The Team Behind Orbitron AI
Founded by an exceptional leadership team of: Former EY Partners and Ex-CTOs and Heads of Engineering from successful Series B+ startups. The founding team brings together seasoned business, product and engineering leaders who have built and scaled large organizations and have led enterprise projects valued at over $1 billion.
Why Join Orbitron AI?
- Massive Impact, Real Ownership → Ship end-to-end features, own outcomes, and see your work power mission-critical systems.
- Battle-Tested Leadership → Learn and grow with seasoned leaders who bring deep industry expertise and zero ego.
- Cutting Edge Stack → Work with Gen AI frontier models, Java, Python, Go, Type Script, GCP & edge compute, Vector DBs, DAG engines.
Our Cultural DNA
- Hustle → Move with urgency, bias for action, celebrate shipping.
- Ownership & Commitment → Act like a founder — your decisions shape the company.
- Founder’s Mindset → Challenge ideas, not people. Seek truth, not consensus.
- Continuous Learning → No legacy thinking; we invest in upskilling and sponsor courses &
Why This Role
Airline crew rostering is one of the most complex real-world optimization challenges, thousands of
constraints, millions of possibilities, and zero room for error.
We’re not looking for someone to maintain spreadsheets or tune simple heuristics. We’re building autonomous decision-making systems that blend classical optimization, AI, and domain intelligence to generate schedules that are safe, efficient, and disruption resilient. You’ll be the technical owner of our optimization engine, modelling constraints, building solvers, running
large-scale experiments and ultimately shipping a system that an entire industry depends on.
Bonus: we’re a company where using AI tools to accelerate your own workflow is not just allowed; it’s expected.
What You Bring
1. Bachelor’s or master’s degree in Operations Research, Computer Science, Industrial Engineering, Applied Mathematics, or equivalent practical experience
2. 5+ years of experience building or operating large-scale optimization or scheduling systems
3. Strong expertise in one or more optimization techniques:
a. Mixed-Integer Programming (MIP)
b. Constraint Programming (CP)
c. Heuristics / Metaheuristics (GA, Tabu, Simulated Annealing, LNS, etc.)
d. Column generation or decomposition methods
4. Hands-on experience with optimization tools like Gurobi, CPLEX, OR-Tools, Mini Zinc, Pyomo,
or custom solvers
5. Ability to model complex rule systems (legalities, constraints, fatigue rules, preferences, unions,
etc.)
6. Proficiency in Python (bonus: experience in C++ for performance-critical components)
7. Familiarity with ML-assisted optimization is a plus (RL, heuristic learning, forecasting, or delay
modelling)
8. Experience designing experiments, tuning solvers, and interpreting model output.
9. Strong understanding of production engineering practices: CI/CD, version control, testing
frameworks, monitoring.
10. Excellent communication and collaboration skills; ability to thrive in a fast-paced, problem-
solving, startup environment.
What You’ll Do
1. Design, build, and optimize the core scheduling engine for airline crew and cabin rosters — from
constraint modelling to solver implementation.
2. Build scalable, parameterized formulations for pairing, rostering, bidding, fatigue compliance,
legality checks, and disruption recovery.
3. Develop and tune algorithms (MIP, CP, heuristics, or hybrids) capable of handling very large
problem instances.
4. Create automated pipelines to run “what-if” scenarios, simulations, and optimization
experiments.
5. Integrate real-world data sources — schedules, crew profiles, aircraft rotations, rule sets — into
the optimization engine.
6. Collaborate closely with product, domain experts, and engineering teams to define rules,
constraints, and objective functions.
7. Experiment quickly: prototype new formulations, prune search spaces, evaluate heuristics, and
benchmark solver performance.
8. Work with AI/ML engineers to blend optimization with predictive modelling, reinforcement
learning, or heuristic learning where beneficial.
9. Help define reliability, feasibility guarantees, and quality metrics for solver outputs.
10. Turn complex airline rules into simple, elegant models that consistently produce feasible and
high-quality schedules.
11. Contribute to building the next generation of autonomous operational decision systems used
inside enterprise environments.
Perks & Benefits
- Top‑Tier Compensation & stock options with substantial potential upside.
- Latest Mac Book Pros + 4 K monitors
- Paid AI tool & cloud credits to experiment freely
- Premium health, wellness, and learning budgets
- A culture celebrating every victory.
- Continuous learning and skill development opportunities.
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