IBM Research
2014 - 2020
Software Development Engineer / Sr. Inventor
Built applied ML and computer vision systems, including multi-node pill detection and media understanding pipelines.
- 17 patents filed, 14 granted
- Research to production track record across global R&D organizations
- Expertise in GenAI, Agentic AI, and enterprise computer vision
AI Leadership Portfolio
Jhilam Bera
AI Architect | GenAI | Computer Vision
17 Patents | Ex Samsung Research | Ex IBM Research | Ex Accenture Labs
Helping organizations build production-grade AI systems and Agentic AI solutions.
Experience
Research and enterprise delivery journey
Accenture Labs
2020 - Jun 2022
R&D Associate Principal
Led Green AI research to estimate energy/carbon footprint trade-offs for ML workloads across AWS infrastructure.
Samsung Research
Sep 2022 - Jan 2024
Sr. Chief Research Engineer
Built bokeh generation pipelines using segmentation and matting; contributions shipped on Samsung A and M series devices.
True ValueHub
Apr 2024 - Nov 2025
Director AI/ML
Built CAD-to-vision embedding pipelines and LLM-driven cost analysis workflows for manufacturing sourcing decisions.
Optum
Nov 2025 - Present
Lead AI/ML
Leading AI engineering for healthcare applications with focus on production reliability, governance, and measurable outcomes.
Projects
High-impact delivery, non-confidential summaries
Case Study
Samsung AI Bokeh
Semantic segmentation and matting based bokeh generation pipeline optimized for mobile deployment.
Case Study
CAD Vision Embedding System
Generated multi-angle CAD views and built custom multi-channel vision embeddings for vector database retrieval.
Case Study
Cost Prediction LLM
Used compact transformer workflows for material-cost document parsing, feature extraction, and should-cost prediction.
Case Study
Green AI Research
Modeled utilization-energy-carbon relationships across cloud instance types to improve infrastructure decisions.
Case Study
Chaos Robo Robustness
Simulated robotics perturbations in ROS/Gazebo environments to estimate robustness under adverse operating conditions.
Case Study
Multi-Modal Enterprise Vision
Built production-oriented computer vision components with scalable serving patterns and enterprise integration.
Patents
Applied invention portfolio
Total Patents
17
Granted
14
Consulting
Fixed-scope engagements
| Service | Price |
|---|---|
| AI Strategy Review | ₹25k+ |
| Architecture Assessment | ₹40k+ |
| Computer Vision PoC | ₹75k+ |
| Agentic AI Development | ₹1L+ |
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Consultation and project discussions
Direct Contact
LinkedIn: jhilam-bera-0ba006b0
GitHub: github.com/Jhilam2014
YouTube: @jhilam3017
WhatsApp: +91 9740488875
Email: jhilam.astro@gmail.com
Phone: +91 9740488875
How Agentic Build Worked this web site
Orchestrator-agent multi-agent workflow
Orchestrator
Parsed goals, decomposed features, created task graph and dependency order.
UI Agent
Built reusable sections, theme-safe styling, and iconography across light and dark modes.
Content Agent
Mapped CV, projects, patents, and consulting details into section-specific narratives.
QA Agent
Ran build checks, found hydration/runtime risks, and shipped stable integration fixes.
Stage 1: Planning
Input requirements, risk checks, and execution plan are created.
Stage 2: Parallel Execution
UI, content, and integration tasks run in parallel with frequent handoffs.
Stage 3: Validation + Ship
Build/test verification gates merge changes into production-ready output.
Detailed Handoff Flow
Orchestrator -> Requirements Parser -> Task Planner -> UI Agent / Content Agent / Infra Agent (parallel) -> Integrator Agent -> Build Validator -> Runtime Fix Agent -> Final QA -> Deployable Build
Extra Curriculum / Hobbies
DIY projects and content
YouTube Channel: youtube.com/@jhilam3017
Featured themes: DIY Astrophotography with Raspberry Pi, Robotics experiments, and AI project walkthroughs.