SA

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.

Open to

Bangalore

Work style

Remote, Hybrid, Onsite

Availability

Available in 60 days

Degree confirmed by MEPCO Schlenk Engineering College
Contact details — on requestProof of Work — on request

Experience

Carvewing Solutions

AI/ML LEAD

Carvewing Solutions · Jan 2026 – Present

Not yet confirmed
  • Doing things like a Forward deployed engineer.
C

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.
A

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.
Altoura

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.
The Apache Software Foundation

Open Source Developer

The Apache Software Foundation · Mar 2022 – Jan 2026

Not yet confirmed
G

Head of Events

Google Students Club MSEC · Feb 2022 – Dec 2025

Not yet confirmed
GirlScript Summer of Code

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.😁💝
L

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
Lyft

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
G

UI UX

Google Students Club MSEC · Sep 2020 – Feb 2022

Not yet confirmed
J

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

Skills 0 proven through work

Also works with

Work Planning and PrioritizationModel Training ExecutionFile Upload HandlingVideo Codecs KnowledgeVideo ProcessingArchitectural DesignNode.js DevelopmentBackend DevelopmentBilling System DevelopmentBuilding Web ApplicationsAWS DeploymentFault-Tolerant System DesignData Partitioning and Segregation DesignScalable System DesignUptime ManagementRequirements AnalysisLow-Latency System OptimizationFallback Mechanism DesignFull-stack DevelopmentData Transfer Utility UsageApplication DeploymentWork EfficiencyChatbot DevelopmentAI-Powered Tool DevelopmentAI Workflow AutomationAI Platform DevelopmentSoftware Development PracticesMachine Learning ApplicationArtificial Intelligence Fundamentals

Proof of Work

Proof of Work

Syed shares this with people who ask. You'll hear back either way.

Education

Bachelor of Engineering, Computer Science

MEPCO Schlenk Engineering College · 2019 — 2023

Verified

Contact details

Contact details

Syed shares this with people who ask. You'll hear back either way.

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