Technical Program Manager · St. Louis, MO
Aditya
Kulkarni
Open to Opportunities
2+ Years TPM
01 ABOUT
AK
Who I Am
I'm a Technical Program Manager who operates closer to a tech lead than a coordinator — I own delivery, design system workflows, and resolve technical blockers, not just track them.
At Spectrum, I drove 3 enterprise AI systems from zero to production — coordinating engineering, architecture, and PMO across the full release lifecycle while building RAG pipelines and LLM workflows alongside the team.
My research background in distributed systems and real-time data pipelines means I can read a system design doc, spot the gaps, and align engineers on tradeoffs — without needing things translated.
0
AI Systems Shipped to Prod
0%
QA Cycle Time ↓
0%
Pipeline Latency ↓
0+
Cross-Functional Teams Led
02 EXPERIENCE
Where I've Worked
Spectrum
Technical Program Manager
- Drove end-to-end delivery of 3 enterprise LLM systems — automated test case generation, code impact analysis, and SDLC workflow automation — from requirements intake through production across 4 engineering teams.
- Co-designed RAG pipeline architecture with engineers: Kafka ingestion → chunked embeddings (AWS Bedrock) → vector store (PostgreSQL) → FastAPI retrieval layer — reducing QA cycle time by ~35%.
- Reduced production pipeline failures by building validation and observability workflows (schema checks, latency monitors, alert thresholds), cutting mean-time-to-detect on data issues by ~40%.
- Coordinated 6 cross-functional teams (engineering, PMO, architecture, security, QA, vendors) across the full AI release lifecycle — from capability scoping to prod deployment and hypercare.
- Eliminated ~30% of manual QA and documentation effort by embedding LLM-assisted workflows directly into developer tooling — freeing engineers for higher-leverage work each sprint.
Spectrum
Technical Project Manager Intern
- Ran Agile execution for 2 concurrent release workstreams — owned Jira backlog, defined acceptance criteria for 40+ user stories, and drove bi-weekly releases with zero scope creep.
- Tightened sprint predictability by restructuring refinement sessions: aligned sprint goals to roadmap milestones and reduced carryover stories by ~20% across an 8-week delivery window.
Univ. at Buffalo
Research Assistant
- Engineered a distributed Kafka message broker for IoT device fleets in Python — improved data transmission throughput by 30% and cut end-to-end pipeline latency by 40% vs. baseline polling architecture.
- Built async WebSocket streaming infrastructure handling high-throughput sensor telemetry — achieving sub-100ms response times and enabling real-time monitoring at scale.
03 PROJECTS
Selected Work
PROJECT_01 · JAN 2025
Data Analytics & Capacity Dashboard
Problem: Engineering teams had no visibility into delivery trends or capacity gaps — planning was reactive and spreadsheet-driven. Built: Python/SQL analytics pipeline feeding 6 Tableau dashboards with rolling forecasting models (6–8 week horizon). Impact: Replaced manual planning process for 3 teams; surfaced utilization bottlenecks before they hit sprint commitments.
PROJECT_02 · FEB 2026
Agentic AI Program Automation
Problem: Routine TPM tasks — research, scheduling, cost estimation — consumed 4–6 hrs/week of manual effort. Built: Multi-agent system on AWS Bedrock where an orchestrator LLM routes tasks to specialist agents (research, planning, cost optimization) via tool-use APIs. Tradeoff: Chose tool-use over fine-tuning for flexibility and zero retraining overhead. Prototype showed ~60% reduction in time-on-task.
PROJECT_03 · NOV 2025
AI-Powered SDLC Intelligence Platform
Problem: QA teams spent 40%+ of sprint time manually writing test cases and mapping code-change blast radius. Architecture: Code commits → FastAPI ingestion → chunked embeddings → pgvector store → AWS Bedrock retrieval → structured test case + impact report output. Tradeoff: RAG over fine-tuning — lower cost, no stale model problem, codebase stays live. Impact: ~35% reduction in QA cycle time; test case generation dropped from ~2 hrs to ~15 min per sprint.
PROJECT_04 · DEC 2025
Real-Time Risk Intelligence Engine
Problem: Program blockers only surfaced during status meetings — 2–3 days after the signal existed. Built: Kafka streaming pipeline → scikit-learn anomaly detector (Isolation Forest) → FastAPI risk-scoring API → Grafana dashboard with per-workstream risk indicators. Impact: Early warnings surfaced 48–72 hrs before traditional status reporting; eliminated surprise blockers across 10+ concurrent workstreams.
04 SKILLS
Tools & Expertise
Program Management
End-to-End Execution
Multi-Workstream Delivery
Risk Mitigation
Dependency Tracking
Capacity Planning
Roadmapping
Stakeholder Reporting
Agile & Process
Agile / Scrum
Jira
Confluence
Sprint Planning
Backlog Prioritization
Release Management
Data & Analytics
SQL
Python
Tableau
A/B Testing
Data Pipelines
Forecasting
Infrastructure & Systems
Kafka
Kubernetes
Jenkins
GitLab
Distributed Systems
Real-Time Processing
Cloud & AI
AWS Bedrock
LLM Agents
WebSockets
IoT Systems
Async Programming
Productivity
Git
MS Excel
PowerPoint
Visio
MS Word
05 HOW I THINK
My Operating System
Ambiguity
I decompose unclear problems into the smallest testable assumption and work forward from there — not from consensus or the loudest voice in the room.
Technical Systems
I read architecture docs the same way I read PRDs: looking for gaps between what the system promises and what the requirements demand. I flag those before the sprint starts.
Cross-Team Execution
Coordination failures are communication failures. I solve them with shared artifacts — decision logs, dependency maps, clear owners — not more meetings.
AI & LLM Workflows
I evaluate AI systems by their failure modes first — hallucination rate, retrieval recall, latency tail cases — before celebrating accuracy on happy paths. Production is not a demo.
Delivery Risk
I treat delivery risk the same way engineers treat technical debt: track it, quantify it, and surface it early — before it compounds into a missed release.
06 CERTIFICATIONS
Credentials & Certs
PMP — Project Management Professional
Project Management Institute (PMI)
Certified SAFe 6 Agilist
Scaled Agile, Inc.
AWS Certified Cloud Practitioner
Amazon Web Services
Deep Learning Specialization
DeepLearning.AI · Coursera
Google Project Management Certificate
Google · Coursera
AI for Everyone
DeepLearning.AI · Coursera
07 EDUCATION
Academic Background
B.S. Computer Science & Engineering
University at Buffalo, State University of New York
GRADUATED · MAY 2025
CS
08 CONTACT
Let's Connect
Open to TPM and Technical Program Management roles at product companies, AI-first teams, and high-complexity engineering orgs. I work best where the problems are hard, the systems are real, and execution actually matters.
Open to Opportunities
St. Louis, MO