Amoghraj Lakkanagavi

Amoghraj Lakkanagavi

As an AI Platform Architect, I thrive on the autonomy to design and implement robust, compliant solutions, driven by a strong sense of accountability and ambition. My idealistic vision guides me in crafting innovative AI platforms, ensuring precision and adherence to the highest standards.

Work style

Remote

Availability

Available in 30 days

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Contact details — on requestProof of Work — on request

Experience

O

AI Platform Architect

Ojas · Present

Not yet confirmed
  • Architected Ojas, a HIPAA-compliant multi-tenant API platform exposing specialised medical AI via a single developer-facing API  with RBAC, credit-based billing, real-time SSE streaming, webhooks, PHI-safe data handling, and GPU-accelerated model serving on GCP Kubernetes.
  • Designed per-request audit logging and tenant data isolation meeting HIPAA technical safeguard requirements  enabling the platform to be used by third-party healthcare developers without compliance risk.
  • Introduced spec-driven development across the team  a custom SDD workflow where PMs defined tasks as structured specs and engineers implemented against them with Claude Code-assisted coding; shipped the full Ojas platform in under 2 weeks.
M

On-device Health Monitoring Lead Engineer

Maí · Present

Not yet confirmed
  • Built continuous vitals monitoring using an on-device SLM  real-time health signal analysis with full offline operation and a context-aware alert engine that surfaces clinically significant patterns while suppressing noise.
  • Integrated smart band over BLE; architected a CDC-compatible [band  device  cloud] sync pattern  decoupling the app from cloud dependency for core monitoring with reliable eventual consistency on reconnect.
O

AI Research Engineer

On-device AI Research & POCS · Present

Not yet confirmed
  • Delivered end-to-end speech-to-speech on-device: ASR  LLM reasoning  TTS  fully local, no cloud round-trip; demonstrated production-grade conversational AI latency on constrained hardware.
  • Built runtime model swapping (vision/text) on-device; implemented on-device function calling pipelines; developed semantic router for intent-to-model dispatch via embedding similarity; validated Gemma models for offline latency-critical deployment.
  • Benchmarked full-system TTFT on-device: ASR + SLM function calling achieves 1-2s; ASR + SLM + VLM pipeline achieves 3-5s  all running on-device with zero cloud dependency.
AjnaLens

Software Developer 2 (AI-CV)

AjnaLens · Feb 2023 – Present

Not yet confirmed
  • ▸ Agentic AI Platform — Consumer Wearables
  • Agentic AI platform for a consumer smart glasses product — multi-step task execution, persistent memory, and MCP integration, deployed on Kubernetes sustaining 10× traffic spikes at p99 under 500ms.
  • Built agent memory for long-horizon continuity and session-aware personalisation; full observability across trace logging, latency dashboards, and error attribution.
  • ▸ On-device AI — Smart Glasses NPU
  • Ported a production ASR model to a wearable NPU — optimised for constrained memory and compute; fully offline, zero cloud round-trip.
  • Built a wake word pipeline: data generation, model training, and NPU inference runtime — enabling hands-free glasses activation on-device.
  • ▸ Clinical AI — Government Hospital Partnership
  • Led AI engineering under a tripartite clinical partnership — full ownership of two medical AI products (dermatology and health screening) from scoping through production.
  • Fine-tuned vision models on clinical datasets from a government hospital; built a PHI-compliant pipeline with encryption, access controls, and audit trails for cross-institution data exchange.
  • Implemented a continuous retraining loop with quality-gated evaluation before any version hit production.
  • ▸ HIPAA-compliant Healthcare AI Developer Platform
  • Architected a HIPAA-compliant multi-tenant API platform exposing medical AI to third-party developers — RBAC, credit billing, SSE streaming, PHI-safe data handling, and GPU-accelerated serving on Kubernetes.
  • Shipped the full platform in under 2 weeks via spec-driven workflow with AI-assisted engineering.
  • ▸ On-device AI Research
  • Delivered end-to-end speech-to-speech on-device (ASR → LLM → TTS) — production-grade latency on constrained hardware, zero cloud dependency.
  • Built runtime model swapping, on-device function calling, and a semantic router for intent-to-model dispatch.
  • Benchmarked TTFT: 1–2s for ASR + SLM; 3–5s for ASR + SLM + VLM — entirely on-device.
A

Lead AI/CV Engineer (SDE II)

Ajnalens · Feb 2022 – Present

Not yet confirmed
  • Architected and productionised an agentic AI platform  multi-step autonomous task execution with scalable backend sustaining high-throughput LLM inference at p99 response times under 500ms; deployed on GKE with horizontal pod autoscaling, sustaining 10 traffic spikes without SLA degradation.
  • Built a persistent agent memory system enabling long-horizon task continuity, session-aware personalisation, and cross-conversation state eliminating repeated context reconstruction overhead.
  • Integrated MCP (Model Context Protocol) and built developer quality-of-life tooling enabling teams to extend agent capabilities without modifying core infrastructure; established full observability  trace logging, latency dashboards, error attribution.
  • Ported Whisper ASR to Qualcomm AR1 Gen 1 NPU  optimised the model for the wearable's constrained memory and compute envelope while preserving transcription accuracy; no cloud round-trip required.
  • Built a custom wake word pipeline end-to-end: synthetic and real data generation, model architecture, on-device classifier training, and NPU inference runtime enabling fully offline hands-free glasses activation.
A

Clinical AI Lead (Indus Derma & Aarogyam)

Ajnalens (in partnership with AIIMS  Google) · Feb 2022 – Present

Not yet confirmed
  • Led the AI engineering team under a tripartite partnership with AIIMS and Google, taking full technical ownership from problem definition through production deployment of two medical AI products: Indus Derma (dermatological diagnosis) and Aarogyam (health screening).
  • Fine-tuned vision models on the SigLIP architecture using Indian clinical datasets from AIIMS, adapting representations to Indian dermatological presentations underrepresented in standard benchmarks  achieving clinically validated accuracy.
  • Built a PHI-compliant secure data pipeline for clinical imaging handover  encryption, access controls, audit trails, and compliance for sensitive medical data transfer between a government hospital and a private organisation.
  • Implemented a Vertex AI continuous retraining loop: clinician-labeled data feeds back automatically into training, with model evaluation gates enforcing quality thresholds before any version reaches production.
AjnaLens

Full Stack Developer

AjnaLens · Feb 2022 – Feb 2023

Not yet confirmed
  • Platform Revamps: Spearheaded major overhauls of collaborative web platforms, taking full ownership of interactive web applications with real-time content editing and cross-functional workflows.
  • End-to-End Development: Led development of large-scale systems from scratch, including frontend architecture, backend APIs, and database design—ensuring high performance, scalability, and user-centric design.
  • Blockchain Integration: Engineered and deployed Ethereum-based smart contracts (ERC20) on Layer 2 solutions like Polygon, handling token deployment, feature planning, and secure integrations.
  • NFT & Web3 Applications: Researched, developed, and integrated NFT trading features including tokenization, swapping, and on-chain listing—interfacing directly with third-party vendors and ensuring seamless UX.
  • Skills & Tools: FastAPI, React, WebVR, Solidity, AWS, PostgreSQL, RESTful APIs, Smart Contracts, System Design, Information Architecture, Tokenomics, Blockchain, and CI/CD pipelines.
T

Flutter Developer

TRiDE Mobility - EV Rides · Oct 2021 – Feb 2022

Not yet confirmed
  • • EV Vehicle Marketplace: Ideated, designed, and developed features for an EV vehicle marketplace, including backend API development and cross-platform UI components in Flutter.
Freelance

Freelance Developer

Freelance · Sep 2018 – Oct 2021

Not yet confirmed

Skills 0 proven through work

Also works with

TensorFlow Lite ConversionFail-Fast StrategyBenchmarking Tool DevelopmentONNX ConversionModel QuantizationAudio Data AugmentationStakeholder ManagementSolution ConsultingProblem SolvingTechnical TroubleshootingApplication DeploymentProduct DevelopmentProof of Concept DevelopmentProduction Deployment VerificationAI Platform Architecture DesignSOP AdherenceTechnical Feasibility AssessmentStrategic PlanningRapid Technology OnboardingAI Agent DevelopmentGenerative AI ApplicationMachine Learning ApplicationAgile MethodologySOP DevelopmentLegal ResearchSecurity ArchitectureCI/CD Pipeline ImplementationCode ReviewStructured LoggingDatabase ManagementSecure Data HandlingNLP Text AnalysisCompliance ManagementWorkflow DigitizationClassification ModelingImage Data AugmentationHyperparameter TuningComputer Vision ModelingTabular Data Cleaning & ValidationData ValidationHandling Data SparsityModel Fine-TuningPeople ManagementWorkflow Automation (Spreadsheet-Based)Dashboard UI DesignTraining Data CreationWeb ScrapingElevenLabs UsageRetrieval-Augmented GenerationText-to-Speech IntegrationSynthetic Data GenerationMachine Learning Model DeploymentBackend DevelopmentProgram ManagementConversational UX DesignDeep LearningImage ProcessingDatabase IntegrationDocument ParsingMachine Data Ingestion DesignClient OnboardingKnowledge Base Design & ManagementAI Solution ImplementationModel Training ExecutionKnowledge Graph ConstructionData PreprocessingUnstructured Data ProcessingLLM-based Data ExtractionMulti-Agent System IntegrationData CollectionAI Platform Development

Proof of Work

Proof of Work

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Education

Bachelor of Engineering, Computer Science and Engineering

Visvesvaraya Technological University (VTU) · 2016 — 2020

Contact details

Contact details

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