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Schedule Interview NowMy name is Danish F. and I have over 5 years of experience in the tech industry. I specialize in the following technologies: Generative AI, Large Language Model, AI App Development, Multimodal Large Language Model, Retrieval Augmented Generation, etc.. I hold a degree in Bachelor of Science (BS). Some of the notable projects I've worked on include: Agentic Research-to-Report System (Multi-Agent with LangGraph), LLM Fine-Tuning (QLoRA) for Customer Support Tone + Compliance, Domain RAG Copilot with Citations + Hybrid Search (Production-Grade), Outbound/Inbound RAG Conversational AI calls for Automating CRMs leads, Face Recognition Cross-platform Mobile app, etc.. I am based in Islamabad, Pakistan. I've successfully completed 10 projects while developing at Softaims.
I'm committed to continuous learning, always striving to stay current with the latest industry trends and technical methodologies. My work is driven by a genuine passion for solving complex, real-world challenges through creative and highly effective solutions. Through close collaboration with cross-functional teams, I've consistently helped businesses optimize critical processes, significantly improve user experiences, and build robust, scalable systems designed to last.
My professional philosophy is truly holistic: the goal isn't just to execute a task, but to deeply understand the project's broader business context. I place a high priority on user-centered design, maintaining rigorous quality standards, and directly achieving business goals—ensuring the solutions I build are technically sound and perfectly aligned with the client's vision. This rigorous approach is a hallmark of the development standards at Softaims.
Ultimately, my focus is on delivering measurable impact. I aim to contribute to impactful projects that directly help organizations grow and thrive in today's highly competitive landscape. I look forward to continuing to drive success for clients as a key professional at Softaims.
Main technologies
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Potentially possible
AIOBC
-Planner → researcher → writer → critic agents -Tooling: web retrieval (where allowed), internal docs, calculators, databases -“Verifier” stage to reduce unsupported claims -Structured outputs: execut
-Data curation: historical chats → anonymization → instruction format -QLoRA fine-tune + hyperparameter search -Safety layer: policy prompts + refusal templates + sensitive-topic filters -Offline eval
-Document ingestion + chunking strategy tuned for long-form PDFs and policies -Hybrid search: dense embeddings + keyword/BM25 + reranking -Answer generation with citations and “show your work” snippet
1) Chatbot RAG-based implementation. 2) Use of different LLM models 3) Capability for fine-tuning models. (Llama 2 and Mistral 7B) 4) Real-time system latency. 5) Memory-Augmented RAG 6) Falcon usi
Used deep learning for face recognition, and later for eye detection
Bachelor of Science (BS) in Robotics & Computer-Engineering
2017-01-01-2021-01-01