Syed Aasim

As an AI/ML Lead with over a decade of experience, I am driven by an unwavering ambition to achieve mastery and deliver impactful solutions. I thrive on the autonomy to lead complex projects, taking full accountability and demonstrating determination to push the boundaries of AI/ML innovation.

Experience

AI/ML LEAD

Carvewing Solutions · Jan 2026 – Present

Not yet confirmed

• Doing things like a Forward deployed engineer.

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Software Engineer – AI/ML Platform (AI/ML Engineering Lead)

Carvewing Solutions LLP · Jan 2026 – Present

Not yet confirmed

• Led execution across backend, AI, CV, and enterprise automation products while coordinating a 5-engineer squad across HRMS, CAD automation, CLM/SAM, financial systems, and visual analytics platforms.
• Recognized as Employee of the Month within the first 3 months for technical execution, ownership, and delivery velocity.
• Co-led an AI-Assisted Project Planning workshop for 100+ attendees across internal teams, clients, partners, and engineering professionals.
• Designed and reviewed production AI workflows spanning multimodal extraction, agentic tool use, evaluation checkpoints, human feedback, safety controls, and measurable reliability/cost trade-offs.
• Defined production GenAI integration patterns for client solutions: secure identity/access boundaries, auditability, data sovereignty, guardrails, versioned prompt/configuration releases, rollback paths, and robust provider-failure handling.
• Designed deployment and retrieval architectures spanning AWS Bedrock, ECS/EKS, OpenSearch/vector-search patterns, Docker/Kubernetes, and self-hosted vLLM/Ollama—balancing latency, throughput, token efficiency, cost, and enterprise data boundaries.
• Translated client requirements into measurable LLM adaptation and evaluation plans, including the appropriate use of CPT, SFT, LORA, QLORA, and PEFT; actively implementing the corresponding fine-tuning experimentation workflow.
• Instrumented production services with structured logs and Prometheus/Grafana dashboards for p50/p95/p99 latency, error rate, throughput, queue depth, and background-job reliability.
• GenAI / Agents: LLM system design, agent orchestration, LangGraph, LangChain, AutoGen, Pydantic AI, tool calling, prompt and context engineering, Mem0 + Redis memory patterns, guardrails, prompt-injection mitigation, human-in-the-loop systems, RAG, embeddings, vector search, retrieval optimization, LLM evaluation and benchmarking, OpenAI APIs, Azure OpenAI, Groq, NVIDIA NIM, Ollama, Qwen, Whisper
• Quality / Observability: Langfuse, structured evaluation and benchmarking, human evaluation, retrieval failure analysis, error analysis, A/B testing, inference cost/latency/throughput optimization, token efficiency, structured logging, metrics, dashboards, OpenTelemetry concepts, production monitoring, robust failure handling
• Built an end-to-end engineering drawing automation platform in FastAPI and React/TypeScript that ingests TIF/PDF files, extracts annotations/symbols/dimensions, detects redline corrections, and presents proposed changes for interactive review and approval.
• Combined OpenCV preprocessing with EasyOCR/PaddleOCR, structural layout parsing, drawing-element identification, and Ollama/Groq-assisted token classification to generate structured correction metadata.
• Built repeatable document-evaluation flows around OCR quality, spatial consistency, token classification, and reviewer feedback; used failure analysis to improve robustness on noisy scanned engineering drawings.
• Traceability: Designed a manifest-first workflow that keeps source drawings immutable and records derivative outputs, annotations, approval state, and audit metadata across the review lifecycle.
• Safety: Enforced human-in-the-loop boundaries so AI performs extraction and recommendation while final engineering correctness remains explicitly user-approved.
• Built deterministic CAD-update and engineering-drawing generation workflows with validation, traceability, and human-review checkpoints.
• Architecture: Designed hybrid cloud/on-prem deployment patterns for customers with strict data-sovereignty and IP-protection requirements, evaluating self-hosted model-serving options for secure inference.
• Challenges solved: Improved robustness on noisy scanned drawings, preserved spatial relationships during extraction, converted irregular tabular engineering inputs into valid vector outputs, and supported secure local validation workflows.
• Tech: FastAPI, Python, React, TypeScript, OpenCV, EasyOCR, PaddleOCR, PyMuPDF, Ollama, Groq, OpenPyXL, XlsxWriter, SolidWorks API.
• Built and launched an enterprise HRMS with React/TypeScript and FastAPI covering employee management, leave, timesheets, multi-manager approvals, attendance, notifications, audit flows, KRA, skills, helpdesk, and administration.
• Integrated an LLM tool-calling layer that converts natural-language requests into controlled actions across leave, employee queries, timesheet status, and administrative workflows.
• Implemented constrained action schemas, validation boundaries, audit logs, and authorization checks around LLM-triggered actions to make enterprise agent workflows safe and reproducible.
• Migration: Orchestrated a zero-downtime SQL Server/MySQL-to-PostgreSQL migration across multiple enterprise data domains, with validation, rollback safety, and million-row-scale workloads.
• Security & reliability: Implemented RBAC, stateless JWT authentication, rate limiting, validation boundaries, auditability, scheduled automations, and production error alerting.
• Operations: Added service and job observability covering p95/p99 latency, failed/retried jobs, scheduled automation health, and production error alerts for post-go-live support.
• Challenges solved: Reconciled inconsistent legacy schemas and orphaned records, preserved approval relationships, reset database sequences safely, and stabilized post-go-live workflows without service interruption.
• Tech: React, TypeScript, shadcn/ui, FastAPI, PostgreSQL, SQL Server, MySQL, JWT, Docker.
• Built an end-to-end diagram-to-EDA reconstruction system that converts PDFs and scanned schematics into structured components, labels, tokens, connectivity mappings, and machine-readable circuit representations.
• Combined OpenCV preprocessing, EasyOCR, PyMuPDF rendering, and Ollama/Qwen-assisted classification behind FastAPI services, with a ReactFlow editor for correction and validation.
• Correctness: Designed an immutable, netlist-first architecture with human validation checkpoints before any reconstructed circuit is accepted for downstream EDA use.
• Challenges solved: Recovered multi-point connectivity from noisy raster inputs, reconciled OCR tokens with spatial geometry, and kept visual edits synchronized with structured netlist state.
• Tech: FastAPI, React, TypeScript, ReactFlow, EasyOCR, OpenCV, PyMuPDF, Ollama, Qwen 2.7B/7B, Python.
• Built an enterprise CLM/SAM platform in FastAPI/PostgreSQL covering contract creation, versioning, metadata and clause extraction, configurable state transitions, multi-party approvals, and immutable audit history.
• Integrated Groq-powered natural-language actions and asynchronous Slack/Teams webhooks so users can create, update, clarify, approve, or reject contracts without leaving chat.
• Workflow integrity: Enforced explicit lifecycle states, approval authorization, immutable event history, and strict API boundaries around every LLM-triggered action.
• Interoperability: Designed migration-ready schemas aligned with Icertis, Ironclad, SAP Ariba, and Conga, plus MuleSoft/Informatica-style NLP transformation pipelines.
• Challenges solved: Preserved auditability across conversational actions, normalized heterogeneous contract formats into one schema, and validated asynchronous integrations through secure local test environments.
• Tech: FastAPI, PostgreSQL, SQLAlchemy, Groq LLM, Slack API, Microsoft Teams API, REST APIs, JSON workflows, Docker.
• Built a live CCTV shoplifting-detection platform using AWS Kinesis Video Streams, FastAPI, and a React/TypeScript monitoring interface with video overlays and real-time alert workflows.
• Combined ByteTrack/BoT-SORT tracking, MediaPipe pose and hand landmarks, trajectory deviation, dwell time, hand-object interactions, and temporal sequence scoring to detect intent beyond frame-level objects.
• Explainability: Integrated AWS Bedrock to turn multi-signal detections into human-readable intent classifications and alert explanations for reviewer validation.
• Challenges solved: Maintained identity and behavioral state across frames, reduced dependence on single noisy signals, and synchronized streaming detections with sub-second UI overlays.
• Tech: AWS Kinesis Video Streams, AWS Bedrock, CVAT, ByteTrack, BoT-SORT, MediaPipe, OpenCV, FastAPI, React, TypeScript.
• Built a microservices-based IT cost-allocation and billing platform in FastAPI/PostgreSQL supporting AS-IS/TO-BE allocation, multi-currency FX, markup snapshots, reconciliation, and auditable financial workflows.
• Developed staged Excel ingestion and high-performance XLSX reporting services with validation-before-commit for inconsistent enterprise finance datasets.
• Reliability: Used Redis/Celery for retry-safe distributed processing, automated failure recovery, idempotent job boundaries, and stable execution of long-running allocations and exports.
• Observability: Defined worker queue-depth, retry-rate, export throughput, and end-to-end p99 job-duration signals for operational dashboards and incident diagnosis.
• Controls: Added defensive validation, transactional commits, rate limiting, and audit-ready snapshots to protect high-concurrency financial writes.
• Challenges solved: Normalized irregular spreadsheets, preserved FX/markup calculations across allocation modes, and made failed background jobs safely repeatable.
• Tech: FastAPI, PostgreSQL, SQLAlchemy, Redis, Celery, Docker Compose, Pandas, OpenPyXL, XlsxWriter.
• Built an AI voice companion in Python/FastAPI integrating OpenAI (chat + speech-to-text), ElevenLabs TTS, Tavus real-time avatar video, and LiveKit sessions, with a persistent Postgres memory store injecting remembered facts and conversation history into each prompt for continuity and personalization.
• Reliability: Designed a provider-guard layer with per-user/per-provider cost caps and deterministic local fallbacks, so the app degrades cleanly (never errors out) when an AI vendor times out or a spend limit is hit.
• Safety: Engineered a safety classifier that routes emergencies to SOS steps, refuses medical diagnoses, and enforces AI-honesty guardrails in code (not just prompts) so the companion never pretends to be human - critical for a vulnerable, elderly user base.
• Shipped a cross-platform Flutter app (edge-to-edge onboarding, email-OTP auth, Google sign-in, voice UI) against a microservices backend behind a Traefik gateway with per-service PostgreSQL isolation and Alembic migrations.
• Containerized services with Docker and deployed to a reproducible cloud environment with CI running automated tests against real Postgres on every change.
• Challenges solved: Handled unreliable, metered third-party AI APIs via cost-capped provider guards with graceful fallbacks; bounded prompt context (top-N memory + recent turns) to control cost and latency; enforced non-negotiable safety routing in code rather than trusting model output.
• Tech: Python, FastAPI, OpenAI, ElevenLabs, Tavus, LiveKit, PostgreSQL, Alembic, Flutter, Dart, Docker, Traefik.
• Built data-dense React/TypeScript products for real-time sports metrics, high-velocity telemetry, CCTV event overlays, schematic editing, and drag-and-drop workflow visualization.
• Used ReactFlow, Redux Toolkit, virtualized lists, and WebSocket listeners to keep complex visual state synchronized with streaming backend events.
• Performance: Optimized render boundaries and state updates for sub-second reactivity across large datasets and frequently changing visual elements.
• Challenges solved: Prevented unnecessary re-renders, preserved graph/editor state during live updates, and unified multiple visualization patterns into reusable frontend architecture.
• Tech: React, TypeScript, ReactFlow, shadcn/ui, TailwindCSS, WebSockets, Redux Toolkit.

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Software Engineer - Backend / Node.js / AI Systems

Altoura altoura.com · Jul 2023 – Jan 2026

Not yet confirmed

• Built production backend services for AltouraLabs using Node.js, TypeScript, Express.js, FastAPI, Flask, Azure services, REST APIs, WebSocket/WebRTC signaling, distributed storage, and identity integrations.
• Worked within AltouraLabs R&D to take backend-driven AI systems and cloud-based digital-transformation products from prototype through production rollout under aggressive delivery timelines.
• Designed and implemented API layers for LMS, frontline web editor, Quest remote assist, HoloLens collaboration, PDF analytics, RAG search, video-to-learning-content pipelines, and internal enterprise AI workflows.
• Developed Node.js/TypeScript backend infrastructure for a Microsoft-Guides-style frontline web editor using Express.js, SCXML/SCJSON processing, ReactFlow-compatible graph data, asset pipelines, validation logic, and distributed storage; delivered a production-ready release in 3 weeks.
• Architected a distributed RAG backend using Azure OpenAI, Pinecone, Cosmos DB, optimized chunking, embeddings, retrieval pipelines, and latency-aware API design, improving enterprise PDF query performance by 1.5x.
• Built automated PDF intelligence pipelines using OpenCV, YOLOv3, unsupervised ML, OCR preprocessing, backend job orchestration, and structured extraction workflows, reducing manual document review time by 3x.
• Implemented backend orchestration for Whisper + GPT multimodal pipelines that converted raw videos into structured LMS-ready content, reducing content creation effort by 4x.
• Built backend signaling, session management, and Azure identity integrations for a frontline remote-assist platform using Agora RTC/WebRTC on Quest devices.
• Developed backend APIs and orchestration logic for a HoloLens frontline copilot with real-time sync, canvas operations, and collaboration workflows, improving team productivity by 2x.
• Orchestrated multi-agent LLM workflows using LangGraph and OpenAI function calling to support tool execution, API chaining, automated decisions, caching, prompt compression, and token-window optimization.
• Designed evaluation-oriented RAG workflows with retrieval tuning, response-grounding controls, and production observability, improving enterprise PDF query performance by 1.5x.
• Created an in-house slide conversion backend that converts PPT into Altoura slide format and supports PDF/ODP ingestion formats not supported by Google Slides, Gamma, or Articulate Storyline 360.
• Built a custom e-learning authoring system with Node.js, TypeScript, FastAPI, SCXML/SCJSON, and ReactFlow for interactive logic rendering, validation, and runtime execution.
• Architected backend systems powering Altoura's LMS ecosystem for reliable ingestion, processing, storage, and delivery of learning modules.
• Consistently shipped high-impact backend features under aggressive timelines with production reliability, scalability, and successful enterprise rollouts.
• Tech: Node.js, TypeScript, Express.js, FastAPI, Flask, React, ReactFlow, Azure OpenAI, Azure Identity, Cosmos DB, Pinecone, OpenAI APIs, LangGraph, Whisper, GPT, Agora RTC, WebRTC, SCXML/SCJSON, OpenCV, YOLOv3.
• AI platform architecture: Designed reusable multi-provider integration patterns across OpenAI, Claude, Gemini, Groq, and self-hosted Llama/Ollama, with provider fallback, cost controls, retrieval evaluation, and latency/throughput observability.

Software Engineer (Machine Learning Engineer & Full stack engineer)

Altoura · Jun 2023 – Jan 2026

Not yet confirmed

• R&D ,dealing with :
• Machine Learning/ Artificial intelligence ,Generative AI (GEN AI) and Prompt engineer and Fullstack developement.

Open Source Developer

The Apache Software Foundation · Mar 2022 – Jan 2026

Not yet confirmed

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Head of Events

Google Students Club MSEC · Feb 2022 – Dec 2025

Not yet confirmed

Mentor

GirlScript Summer of Code · Feb 2022 – Dec 2022

Not yet confirmed

• Tech-Team Lead at CodeIN Community.🧑‍💻💻
• Played a Major roll in Reviewing codes and merging pull requests.🚀
• Guided a lot of newbies and Juniors as well.🖤
• Had a ton load of fun.😁💝

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Software Engineering Virtual Experience

Lyft Virtual Software Engineering Program · Sep 2022 – Sep 2022

Not yet confirmed

• Completed backend software architecture, object-oriented design, refactoring, unit testing, and vehicle booking workflow modules using Python, UML, Git, and GitHub.
• Certificate

Back End Developer VEP

Lyft · Sep 2022 – Sep 2022

Not yet confirmed

• Over the period of September 2022, completed practical tasks in:
• Software Architecture
• Refactoring
• Unit Testing
• Test-Driven Development

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UI UX

Google Students Club MSEC · Sep 2020 – Feb 2022

Not yet confirmed

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Software Engineering Virtual Experience

JPMorgan Chase - Virtual Software Engineering Program · Dec 2021 – Dec 2021

Not yet confirmed

• Completed modules involving stock price data feed integration, trader-facing visualization, Python tooling, NumPy/SciPy/Scikit workflows, Streamlit, OOP, testing, Git, and refactoring.
• Certificate