Principal Engineer

Job Description

Responsibilities

Technical Ownership –

• Own end-to-end architecture and technical quality across Stryke, StrykeX, WizOps, and new products.

• Write, review, and approve production code — including AI-generated code — and validate business and trading logic for correctness.

• Design and operate asynchronous and background processing systems (Celery, task queues, schedulers) that power execution, signals, and data pipelines.

• Define and enforce deployment standards: what must be tested, reviewed, and reported before any code reaches production.

• Diagnose and resolve complex issues across the backend, infrastructure, and data layers.

• Own performance, reliability, scalability, and security of our systems.

AI / ML Leadership –

• Lead and mentor the AI/ML team, setting direction for model development, training, deployment, and monitoring.

• Own the architecture for building and serving ML models in production trading products — from data pipelines and feature stores through to inference and monitoring.

• Oversee the use of vector databases and retrieval systems powering AI features (e.g. the StrykeX AI Copilot).

• Establish standards for model evaluation, versioning, drift monitoring, and retraining.

• Ensure AI/ML systems are reliable, explainable where required, and aligned with our SEBI regulatory constraints — no fabricated data, no unauthorized advice.

Team Leadership –

• Lead, mentor, and manage the backend and AI/ML developer teams across all products.

• Set engineering best practices, code review standards, and development workflows.

• Plan and allocate engineering effort across products and priorities.

• Help hire, grow, and raise the bar of the entire engineering team.

Required Qualifications

Backend & Systems –

• Strong hands-on expertise in Python backend development.

• Deep experience with databases and data stores — PostgreSQL, MongoDB, Redis (and ideally time-series stores such as QuestDB).

• Hands-on experience with Celery and asynchronous/background task processing at scale.

• Solid understanding of system architecture, APIs, and the full development-to-deployment lifecycle.

• A track record of shipping and operating reliable, high-availability systems — ideally in fintech, trading, or another high-stakes domain.

AI / ML –

• Hands-on experience with ML frameworks — PyTorch and/or TensorFlow — for building and training models.

• Strong command of the Python ML/data ecosystem — NumPy, pandas, scikit-learn.

• Experience deploying and serving models in production (e.g. FastAPI / model-serving frameworks, ONNX, containerized inference).

• Practical experience with LLMs and the surrounding tooling — prompt design, RAG pipelines, embeddings, and orchestration frameworks (e.g. LangChain or similar).

• Experience with vector databases (e.g. Pinecone, Weaviate, pgvector, Qdrant, or similar) for retrieval and semantic search.

• Understanding of the full ML lifecycle (MLOps) — data preparation, feature engineering, training, evaluation, deployment, and monitoring.

Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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