AISamples

AISamples

1. create own model using reLU

2. Fine tune model: stretegies

3. RAG implementation stretegies

4. RelU vs SoftMax Vs CrossEntropyLoss

5. Different ways to generate token

6. Make the GitHub repo public and build the vLLM inference optimization project (Week 1-3) This is the highest-leverage action. Convert the PyTorch Conference talk into a comprehensive, well-documented open-source project with reproducible benchmarks. This simultaneously addresses the open-source gap, demonstrates technical depth, creates a citable artifact, and directly maps to Red Hat's #1 priority. Include a comparison of PagedAttention, speculative decoding, and continuous batching on Granite and Llama models. Target 3,000+ lines of well-documented code with a professional README, CI/CD, and containerized deployment.

7. Submit a workshop paper to NeurIPS 2026 or ICML 2026 based on the vLLM work (Week 2-5) Convert the inference optimization benchmarking into a rigorous workshop paper. Focus on a novel finding — for example, systematic analysis of speculative decoding strategies across model families, or optimal quantization-aware serving configurations for enterprise workloads. A workshop acceptance at a top venue is achievable within 3-4 months and immediately addresses the publication gap. Co-author with a collaborator who has a publication track record if possible.

8. Build and publish a model quantization/compression pipeline project (Week 3-6) Create a project demonstrating GPTQ, AWQ, SmoothQuant, and structured pruning using both Red Hat's LLM Compressor and Qualcomm's AIMET. Benchmark accuracy vs. latency vs. model size tradeoffs. This is the single most important technical demonstration for Qualcomm and a Tier 1 skill for Red Hat. Include INT4 and FP8 quantization with accuracy analysis on standard benchmarks.

9. Contribute meaningfully to vLLM or InstructLab open-source projects (Week 1-8, ongoing) Submit 3-5 substantive pull requests to vLLM (bug fixes, performance improvements, documentation) or InstructLab (taxonomy additions, training pipeline improvements). Getting merged PRs on these projects gives Akhil instant credibility with Red Hat's hiring team. Join the vLLM Slack community, participate in community calls, and build relationships with the Neural Magic team in Boston. For Qualcomm alignment, contribute to AIMET or ExecuTorch repositories.

10. Build the ExecuTorch on-device inference demo with Qualcomm QNN backend (Week 4-7) Leverage existing JPMC ExecuTorch experience to create a public demonstration deploying a small language model (Phi-3 Mini or Llama 3.2 1B) via ExecuTorch targeting Qualcomm QNN. Profile NPU vs. CPU performance, demonstrate quantization-aware deployment, and benchmark against ONNX Runtime and TFLite alternatives. This is the single most important project for Qualcomm candidacy.

11. Rewrite the resume to emphasize research leadership and quantify team impact (Week 1-2) Restructure the resume around four themes: (a) research output (patent, conference talks, upcoming publications), (b) team leadership with specific numbers ("Led team of X researchers/engineers, set Y research priorities, delivered Z production models"), (c) technical depth in inference optimization, quantization, and on-device AI, and (d) enterprise AI deployment at scale. Add a "Selected Research & Publications" section prominently. Remove generic engineering descriptions and replace with specific metrics and novel contributions.

12. Build a Kubernetes-native ML deployment project using OpenShift AI patterns (Week 5-8) Demonstrate KServe model serving, Kubeflow training pipelines, and GPU scheduling with Kueue. Deploy a fine-tuned LLM with autoscaling, canary rollout, and monitoring. Containerize with Podman. This project directly addresses the infrastructure gap for Red Hat and demonstrates that Akhil understands production AI deployment beyond notebook-level experimentation.

13. Write 2-3 technical blog posts on Medium or a personal blog (Week 3-10) Publish detailed technical posts on: (a) "What I Learned Optimizing vLLM Inference for Enterprise Fraud Detection" — bridges JPMC experience with Red Hat technology, (b) "Deploying PyTorch Models On-Device with ExecuTorch: Lessons from Production" — bridges JPMC experience with Qualcomm's mission, (c) "The State of Model Compression: GPTQ vs. AWQ vs. SmoothQuant in Practice" — demonstrates quantization expertise. These posts build public thought leadership and are discoverable by recruiters.

14. Secure 1-2 additional conference speaking engagements at AI infrastructure events (Week 4-12) Target KubeCon/CloudNativeCon (Red Hat's core community), Snapdragon Summit or Edge AI Summit (Qualcomm's community), or MLOps World. Submit CFPs based on his vLLM work, ExecuTorch deployment experience, or AI observability patent. Two conference talks plus the PyTorch Conference appearance creates a credible speaking portfolio.

15. Build relationships with Red Hat and Qualcomm AI teams (Week 1-12, ongoing) Engage with Red Hat AI engineers on vLLM GitHub issues and Slack. Follow and interact with Steven Huels (VP AI Engineering), Red Hat Brian Stevens (AI CTO), Red Hat and the Neural Magic team on LinkedIn. For Qualcomm, engage with the Qualcomm AI Research team's published papers — cite them, discuss their work publicly, and attend Qualcomm Innovation Fellowship events. Networking at this level matters enormously for director-level hires, which are rarely filled through cold applications alone.