AI Engineer

Sports have an access problem.

For decades, elite performance intelligence, tactical analysis, and scouting infrastructure have been accessible only to the top clubs. Everyone else has been left behind.

StepOut is changing that.

We are building an AI-powered football intelligence platform that transforms raw match footage into structured performance data, tactical insights, and scouting intelligence using proprietary computer vision and machine learning.

We are building this from India, while our technology is being used by clubs like Real Madrid and AFC Ajax, along with leading football ecosystems across 29 countries globally.

If football means something to you beyond entertainment, this might be your place.

• Build and integrate LLM-powered agentic systems, including tool use, function calling, structured outputs, and multi-step reasoning workflows.
• Design RAG pipelines using embeddings, vector search, semantic retrieval, and football data interpretation.
• Build AI/ML capabilities exposed through REST APIs and backend services.
• Evaluate and improve model quality, reliability, latency, and cost.
• Work closely with Engineering and Product to take AI systems from prototype to production.
• Debug imperfect data, failure cases, and system behaviour across AI and backend workflows.
• Stay up-to-date with developments in LLMs, machine learning, and football analytics.

• Strong Python and software engineering fundamentals.
• Strong SQL, NoSQL, data manipulation, analytical thinking, and debugging ability.
• Practical understanding of LLMs and Generative AI.
• Working knowledge of prompt engineering, embeddings, RAG, vector search, and semantic search.
• Experience with agentic AI concepts such as tool use, function calling, and multi-step workflows.
• Ability to build and consume REST APIs.
• A production mindset: maintainable systems, reliable outputs, and sensible trade-offs around latency and cost.
• Genuine interest in football and sports.

• Experience with NLP, Transformers, or multimodal AI.
• LLM fine-tuning, LoRA/PEFT, structured outputs, or AI agents.
• LangChain, LangGraph, LlamaIndex, or similar frameworks.
• Vector databases such as Pinecone or similar.
• FastAPI, Django, or Flask.
• Docker and cloud platforms such as AWS, GCP, or Azure.
• Familiarity with football analytics metrics such as xG, xA, xT, PPDA, or VAEP.

You will thrive here if you:

• See football as more than just a sport.
• Enjoy building systems from scratch, not just experimenting in notebooks.
• Can move through ambiguity and make sound engineering decisions.
• Care about what happens after a model or workflow is deployed.
• Get excited by the challenge of making AI genuinely useful for football.

• Build AI systems at the intersection of football, data, and engineering.
• Work on products used by elite football organizations globally.
• Solve meaningful technical problems with direct impact on how football is understood.
• Help build world-class football infrastructure from India.