Senior Technical Architect

Senior Architect (AI Computer Vision & Sports Intelligence Platform)

Company Description

str8bat is a deep-tech sports technology company building the next generation of sports intelligence infrastructure. Starting with cricket, we convert broadcast video, motion data, and contextual match information into real-time performance intelligence and storytelling for broadcasters, teams, and fans.

Our mission is simple: help players Play Better by turning motion into actionable insight. This role sits at the intersection of AI, computer vision, real-time systems, broadcasting, and human-motion intelligence and very few teams anywhere are solving it at this depth.

Role Overview

We are hiring a deeply technical, hands-on Senior Architect to design and build str8bat's Sports Intelligence Platform end to end and lead from the front. You will own the architecture spanning computer vision, AI orchestration, real-time video, and event-driven systems, translating an ambitious product vision into production systems that hold up under live broadcast conditions.

Your mission: take a broadcaster's live video feed, reconstruct the bat path and its descriptors, compare them against str8bat's library of ~23 million sensor-captured motion records, and estimate sensor-grade metrics bat speed, impact speed, and a timing index then fuse bat path with body and weight-transfer cues into deeper motion intelligence.

Key Responsibilities

Reconstruct bat path and bat trail from a broadcaster's video feed pose estimation, bat tracking, temporal tracking robust to zoom changes, camera movement, inconsistent framing, motion blur, and partial occlusion.

Design the descriptor layer that turns each reconstructed bat path into a structured, comparable representation (geometry, velocity profile, impact point, timing).

Build the comparison-and-estimation engine that aligns those descriptors against str8bat's library of ~23 million sensor-captured motion records to estimate sensor-grade metrics bat speed, impact speed, and timing index none of which are directly measurable from the video.

Fuse bat-path signals with body pose and weight-transfer cues to produce higher-order motion intelligence and shot-quality insight.

Extend the intelligence layer into player-baseline comparison, contextual prediction, temporal trends, and pressure-based performance analysis.

Own the end-to-end, event-driven platform architecture, built around canonical sports event objects, and the synchronization layer across video, metadata, scoring, and motion.

Define the near-real-time processing infrastructure and scalable APIs that broadcasters and downstream systems consume.

Design scalable inference pipelines for live / near-live deployment, optimized for latency and production robustness; architect vector search, embeddings, temporal sequence modeling, retrieval, and hybrid AI + rules-based systems that scale across sports.

Lead technical execution across the stack, orchestrate agentic, AI-assisted development workflows, and build and mentor a lean, high-leverage engineering team.

Partner with product and broadcasters to turn live-production constraints into systems consumable by broadcast graphics, commentary, OTT, and fan-engagement platforms.

Required Technical Skills

Strong systems-architecture fundamentals, with the range to work across both research and production.

Deep computer-vision and ML expertise: Python, PyTorch / TensorFlow, OpenCV, video-processing systems, and real-time inference pipelines.

Event-driven, streaming, and distributed systems; GPU inference optimization; vector databases and retrieval systems.

Hands-on with pose estimation, object tracking, temporal modeling, embeddings and similarity search, and video analytics.

Cross-modal alignment / domain adaptation relating video-inferred motion to sensor (IMU) data, and estimating physical quantities from indirect or weakly-supervised visual input.

Sharp debugging instincts and a first-principles problem-solving mindset.

Strong Plus

Sports analytics, broadcast / media-tech, or multi-camera synchronization experience.

AI-assisted / agentic engineering workflows.

Building scalable production AI infrastructure, and working with large-scale behavioral or motion datasets.

Engineering Mindset We Value

Systems thinking over isolated model-building.

Ability to simplify complexity and own outcomes end to end.

Speed of execution and comfort operating in ambiguity.

Deep curiosity about human movement and performance.

Pragmatism over academic perfection.

Target Candidate Profiles

CV / ML architect who has shipped real-time video-understanding systems to production.

Markerless motion-capture or sports-biomechanics engineer comfortable with 3D human and object pose from monocular video.

Staff / Principal engineer from a deep-tech, sports, or media-tech startup who has owned platform architecture end to end.

ML systems engineer strong in retrieval, embeddings, and inference at scale.

Hands-on engineering leader who has built and run lean, high-leverage teams.

Why This Role Matters

This is one of the first event-centric sports intelligence platforms and the opportunity reaches far beyond cricket analytics. Combining motion understanding, contextual intelligence, and historical correlation can become foundational infrastructure for broadcasting, coaching, performance analysis, fan engagement, and multi-sport human-motion intelligence.

Role Details

Location:Bangalore

Employment type:Full-time employee

Reports to:CEO