Hi, I'm Taaran Jain
I build intelligent systems — from fine-tuned LLMs and RAG pipelines over vector databases to computer vision models and end-to-end ML platforms. Turning data into decisions, and research into real-world products.
01 / About
About Me
I'm obsessed with making machines learn — and making sure what they learn is actually useful.
Hey! I'm Taaran, an AI Engineer passionate about building systems at the intersection of large language models, deep learning, and real-world applications.
I work across the full ML lifecycle — from exploratory data analysis and model research to serving models at scale in production. My current focus is on LLM-powered applications: retrieval-augmented generation (RAG), agentic AI, and multimodal systems.
When I'm not training models or writing code, I'm reading the latest papers on arXiv, contributing to open-source AI tools, or experimenting with new architectures.
6
Products Shipped Live
100%
With Demo & Source
3
Companies Shipped For
2
Papers Published
LLM Engineering
Building RAG pipelines over vector databases, orchestrating agentic workflows with LangGraph, and deploying production-grade LLM applications.
Data Science
End-to-end ML pipelines — from raw data ingestion and feature engineering to model evaluation.
MLOps & Deployment
Taking models from notebook to production with robust CI/CD, monitoring, and scalability.
Research-Driven
Staying close to SOTA research and translating cutting-edge ideas into practical systems.
02 / Projects
Selected Work
Every project below is live, with a public demo and full source code.
FeaturedNexus AI
A multi-modal AI platform combining RAG, live web search, and six LLMs (Llama, Mixtral, Gemma, DeepSeek) in one interface — with real-time streaming, document upload, voice input, and LangGraph-powered node orchestration. Entire stack runs at zero cost.
Featured
FeaturedSelf-Driven Car
A neuroevolution simulator where AI learns to drive using NEAT — evolving neural networks across generations through mutation, crossover, and speciation. A 9-input network (8 ray-cast sensors + velocity) learns steering and acceleration with zero explicit rules.
03 / Experience
Work Experience
Shipping AI systems in production across three companies.
AI Engineer
e-Marketing.io
Sole AI Engineer responsible for designing and shipping the full suite of AI-powered products for a performance marketing agency and its clients — owning everything from problem scoping and model selection to deployment and iteration.
- ▸Built an AI Meeting Summarizer that converts hour-long recordings into structured briefs with decisions and action items — saving teams hours of follow-up every week.
- ▸Developed a Keyword Analysis Engine that replaced gut-feel content decisions with NLP-driven insights on trends, intent, and competitor gaps.
- ▸Shipped AI chatbots across social media and messaging platforms that qualify leads and respond instantly — keeping client pipelines active around the clock without human intervention.
- ▸Delivered a Lead Management Dashboard giving clients real-time visibility into pipeline status and full bot conversation history — everything in one place.
- ▸Built a WhatsApp Task Delegation system where teams assign, track, and close tasks via text or voice note — eliminating app-switching and keeping everyone accountable.
Data Science Specialist
LogiScope Technologies Pvt. Ltd.
Focused on making sense of massive, noisy log data — building ML and deep learning backends that turned raw system logs into actionable intelligence for monitoring and reliability teams.
- ▸Ran deep EDA on large-scale log datasets to surface hidden patterns that manual monitoring consistently missed.
- ▸Built and deployed anomaly detection models using ML and DL algorithms that flagged system irregularities in real time — strengthening monitoring before issues escalated.
- ▸Experimented across multiple analytical techniques to identify the most reliable signals within complex log behaviour, turning raw data into clear, actionable outcomes.
- ▸Collaborated with cross-functional teams to integrate findings into data processing pipelines and improve anomaly reporting frameworks end-to-end.
Data Science Intern
Celebal Technologies
Worked within a professional data science team during a summer internship — getting hands-on with the full pipeline from raw data to deployed models, and applying Azure Cloud to bring it all together in a real production context.
- ▸Cleaned and preprocessed large datasets end-to-end, ensuring model inputs were reliable before a single line of training code ran.
- ▸Implemented ML algorithms against real business problems — moving from experimentation to predictive models with measurable outcomes.
- ▸Leveraged Azure Cloud services to understand how production-grade data applications are architected and deployed at scale.
- ▸Collaborated closely with senior data scientists, absorbing best practices and contributing to cross-functional project delivery.
04 / Research
Research
Papers I've published — at the intersection of AI, financial markets, and healthcare.
A Review of Deep Reinforcement Learning Techniques in Algorithmic and Quantitative Trading
A systematic review of DRL methods applied to algorithmic trading — benchmarking frameworks like AlphaOptimizerNet, QTNet, and FinRL against challenges of market volatility, transaction costs, and the exploration-exploitation tradeoff. Evaluates DDQN and RDMM approaches and outlines what is still needed before DRL systems are production-robust.
AI-Driven Medical Diagnostic System: Incorporating Deep Learning for a More Effective Healthcare Model
Presents a multi-modal diagnostic system combining deep learning and NLP to automate disease detection, medical image analysis, and drug identification. Integrates Gemini for real-time AI insights on a Django/React Native stack — targeting reduced diagnostic time, human error, and cost, especially in remote and underserved healthcare settings.
05 / Skills
Technical Skills
What I reach for, grouped by how deeply I have actually used it.
LLM / NLP
8Machine Learning
5Deep Learning
3Languages
2Backend & APIs
4Databases
4Cloud & DevOps
3Data Visualisation
306 / Leadership
College Leadership
Leading teams, running events, and building technical communities.
AI/ML Lead
GDSC Poornima
Sep 2023 – Aug 2024
Owned the AI/ML vertical at GDSC Poornima for a full year — setting the technical direction, building the team's capabilities, and making machine learning genuinely accessible to the wider student body.
- ▸Turned abstract AI/ML research into hands-on projects that students could build, ship, and learn from.
- ▸Ran workshops that went beyond slides — giving peers practical exposure to tools and workflows used in industry.
- ▸Became the go-to mentor for students navigating ML projects and coursework, accelerating their learning curve.
- ▸Kept the AI/ML stream tightly integrated with GDSC's broader mission, ensuring every initiative moved the needle.
Student Ambassador — IDEA Lab
AICTE
Sep 2022 – Jul 2023
Represented AICTE's IDEA Lab on campus — connecting students with cutting-edge hardware like IoT, 3D printers, 3D scanners, and laser cutters, and turning raw curiosity into finished projects.
- ▸Ran workshops and hackathons that got school and college students building with real tools, not just reading about them.
- ▸Mentored students from first idea to working prototype — bridging the gap between imagination and execution.
- ▸Partnered with faculty to keep lab activities academically grounded while still pushing the boundaries of what students attempted.
Campus Ambassador
HackerEarth
Mar 2023 – Mar 2024
Was the face of HackerEarth on campus for a year — turning a platform into a movement by getting students to compete, collaborate, and grow as developers.
- ▸Built a thriving coding culture on campus through competitions and hackathons that pushed students beyond their comfort zone.
- ▸Forged partnerships with college clubs to extend HackerEarth's reach far beyond a single department.
- ▸Acted as the feedback loop between students and the platform — surfacing real insights that shaped better events.
07 / Certifications
Certifications
Formal training across AI, ML, and cloud — foundations through production.
WorldQuant Challenge — Silver Certificate
WorldQuantAwarded for strong performance in WorldQuant's quantitative research challenge — applying data-driven and algorithmic thinking to real financial markets.
Financial Analyst Career Track
365 Financial AnalystFinancial modelling, valuation, and data-driven investment analysis — adding a quantitative finance lens to complement my ML engineering background.
MLOps Specialization
DeepLearning.AI · CourseraBridges the gap between model training and production — CI/CD for ML, data pipelines, model monitoring, and scalable deployment practices.
TensorFlow: Advanced Techniques Specialization
DeepLearning.AI · CourseraGoes beyond standard TensorFlow — custom model architectures, advanced CV pipelines, and performance optimisation for production deployment.
08 / Contact
Get In Touch
Have a role, a product, or a research problem in mind? I reply to every message.
Let's build something intelligent
I'm open to full-time AI Engineer, Machine Learning Engineer, Data Scientist roles, plus freelance projects and research collaborations. If you have a problem involving data or intelligence, tell me about it.
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Common Questions
The things people usually ask before getting in touch.
Full-time AI Engineer, Machine Learning Engineer, Data Scientist roles. I also take on freelance projects and research collaborations where the problem is interesting.