I build engineering organisations, developer platforms, and AI-enabled workflows that help teams ship faster without compromising reliability, security, or engineering discipline.

Over the past decade, I have worked across engineering leadership, backend systems, platform engineering, DevSecOps, test automation, video infrastructure, and applied AI. My focus today is on turning AI from an individual productivity tool into a dependable engineering capability used across teams.

• Lead a 30-member engineering organisation across five cross-functional teams
• Drive engineering strategy, organisational design, hiring, mentoring, delivery, and operational excellence
• Build and scale AI-assisted software-development workflows
• Design Claude Code skills, commands, agents, and reusable engineering playbooks
• Build Model Context Protocol (MCP) servers, integrations, and security controls
• Experiment with MCP firewalls, RAG pipelines, agentic workflows, and AI-powered internal tools
• Architect backend and platform systems using Python, Django, AWS, Docker, Kubernetes, and distributed-system patterns
• Lead recurring security and compliance programs, including annual SOC 2 certification, VAPT, Trusted Partner Network (TPN) compliance, secure development practices, and production-incident management

• Transformed engineering delivery from periodic releases to a daily deployment model
• Scaled test automation coverage from 1 platform to more than 15 platforms
• Reduced a critical web-automation suite's execution time from 8 hours to approximately 1 hour
• Introduced organisation-wide AI-assisted development practices for code review, testing, security remediation, documentation, and full-stack delivery
• Built custom AI workflows using Claude Code, MCP, Figma integrations, automated reviews, and team-specific engineering commands
• Led engineering governance around enterprise AI adoption, including usage policies, licensing, analytics, security boundaries, and cost controls
• Drove annual SOC 2 certification, coordinating engineering controls, evidence, remediation, and cross-functional readiness
• Led VAPT programs and remediation, translating findings into accountable engineering work and closure
• Drove Trusted Partner Network (TPN) compliance and broader security-readiness initiatives for media and enterprise customers
• Directed production-incident response and engineering improvements across frontend, backend, QA, DevOps, video, and platform functions

Acknowledged in a validation study published in The Lancet Regional Health – Southeast Asia for contributing to data-security measures for the AI-powered Child Growth Monitor.

This was a technical contribution acknowledged by the study team, not research authorship.

View the publication

Delivered “Migrations That Bite”, a practical talk about Django migration failures, production risks, and safer migration practices.

View the DjangoDay India 2025 event

• Represented my organisation at PyCon India 2024
• Attended PyCon India 2013, 2014, and 2015
• Volunteered with the PyCon India community during my early engineering career

I am currently building and experimenting with:

• Reusable Claude Code skills and commands
• AI-assisted code review and engineering-governance workflows
• Agentic development systems with explicit human-review boundaries
• MCP servers for securely exposing internal tools and APIs
• MCP firewall concepts for policy enforcement, access control, tool validation, and data-loss prevention
• RAG pipelines for internal engineering knowledge and operational context
• AI-assisted testing, documentation, remediation, and developer enablement
• Secure enterprise adoption patterns for coding agents and LLM-powered workflows

My leadership work spans:

• Engineering strategy and execution
• Team topology and organisational design
• Hiring, mentoring, performance, and career development
• Architecture and technical decision-making
• Production reliability and incident management
• Security, compliance, and risk reduction across SOC 2, VAPT, TPN, secure SDLC, and audit readiness
• Developer productivity and release engineering
• Cross-functional alignment across product, engineering, QA, security, and operations

Core: Python, Django, Flask, Node.js, JavaScript, REST APIs, distributed systems
AI engineering: Claude Code, Codex, MCP, RAG, agentic workflows, prompt and context engineering
Cloud and platform: AWS, Azure, Docker, Kubernetes, Linux, Nginx, uWSGI, serverless systems
Data and infrastructure: PostgreSQL, MySQL, MongoDB, Redis, Celery, Elasticsearch, ELK, Spark
Delivery and security: CI/CD, DevSecOps, annual SOC 2 certification, VAPT, TPN compliance, audit readiness, observability, incident response, secure SDLC
Media systems: FFmpeg, video processing, streaming systems, TURN/STUN, real-time communication

I have contributed fixes and features across the Python/Django, web, and developer-tooling ecosystem.

• Fedora Websites — contributed website standards and validation improvements
  View contribution

• Waartaa — contributed to the administration interface
  View pull request

• Django Searchable Select — added support for Django 1.11
  View pull request

• Dante2 — contributed product features and improvements
  View pull request

• System Designs — scalable architecture notes, design exercises, and engineering case studies
• Video Transcoding and Transmuxing Pipeline — Python and FFmpeg-based media-processing pipeline
• Kubernetes Showcase — container deployment, services, routing, and Kubernetes fundamentals
• CRUD API Performance Comparison — benchmarking and comparison of C and Python API implementations
• ML and AI Experiments — practical machine-learning and computer-vision experiments

My path has moved from hands-on Python and Django development into platform engineering, senior backend engineering, engineering management, organisation leadership, and applied AI leadership.

I still stay close to architecture and implementation. I believe engineering leaders should understand the systems, constraints, risks, and trade-offs behind the decisions they ask teams to execute.

• AI should strengthen engineering judgment, not replace it
• Fast delivery without operational discipline creates delayed failure
• Security and compliance should be built into engineering workflows
• Architecture should remain understandable and verifiable by humans
• Leaders should create systems in which teams can make good decisions independently
• Developer productivity should be measured through outcomes, not tool usage
• Reliable software is a product of technical choices, team design, and operating discipline

• GitHub: github.com/sheeshmohsin
• LinkedIn: LinkedIn
• Blog: sheeshmohsin.wordpress.com
• Stack Overflow: Sheesh Mohsin

I am interested in engineering-leadership, Head of Engineering, Director of Engineering, AI Engineering Leadership, and platform-leadership opportunities where I can combine organisational leadership with deep technical execution.