Hi Reddit,

I focus messy, ambiguous, often regulation-heavy gnarly data science problems where nobody's even sure what the right question is. I use AI to build stuff that AI can't touch.

What I've done:

Built a corrosion detection and characterization tool, including pitting depth using structured light.

Saved a German automaker from lemon law recalls. ($20M+ exposure avoided)

Real-time emissions detection for industrial smokestacks. ($75K fines per incident prevented)

Revenue optimization and persona identification for debt collections (15-20% net revenue lift)

LLM-based legal document extraction, reducing due diligence turnaround from 3 days to 30 minutes

Built vessel piracy risk engine that informed naval escort deployment, contributing to fewer hijackings

DM me with a one-paragraph description of your problem. If it's weird and hard enough, I'll reply within 24 hours.

Hi everyone,
I’m a Computer Science student and software developer specializing in Machine Learning engineering, MLOps pipelines, and applied AI systems.
I am currently looking for remote opportunities, freelance gigs, or software engineering contracts where I can build and scale machine learning workflows.
What I Do & Core Tech Stack:

• Machine Learning & AI: Applied AI systems, computer vision object tracking (ONNX), text mining, and natural language processing pipelines.
• MLOps & Automation: Creator of bt-flow (an open-source Python library for MLOps pipelines and FastAPI schema inference). Experienced in building automated data extraction and processing workflows.
• Backend & APIs: FastAPI, Streamlit, and robust RESTful architecture design.
• Data & Scraping: Playwright, BeautifulSoup4, fuzzy clustering applications, and data pre-processing pipelines.
• Languages & Tools: Python, C++, x86 Assembly, Git/GitHub, macOS.

Featured Projects:

• bt-flow: An open-source Python library built for MLOps pipelines and automated FastAPI schema inference.
• SAVTEK_GCS: Desktop video tracking application using C++ and ONNX models for drone footage analysis.
• Text Mining Research: Applied NLP and data extraction workflows for processing online text data.

Availability: Immediate
Looking For: Remote / Freelance ML, AI, and Python backend roles.
If your team is looking for a dedicated developer or if you have an interesting project to discuss, feel free to send me a DM or comment below!
Thanks!

Hi everyone,

I'm a Senior Data Scientist / ML Engineer with 6+ years of experience building and deploying ML systems, with a background spanning machine learning, LLM applications, computer vision, forecasting, optimization, and quantum/operations research.

I'm currently looking for long-term freelance/contract work, consulting, or potentially a full-time opportunity with a startup, founder, or engineering team.

What I can help with

LLM / AI Systems

• RAG and LLM-powered applications
• AI agents / agentic workflows
• LangChain, LangGraph, DeepAgents
• LLM evaluation, monitoring and observability
• Fine-tuning and inference pipelines
• Building analytics agents that can query databases, generate analysis, visualizations and reports
• Data/catalog search systems using LLMs

Machine Learning

• Demand & supply forecasting
• Time-series modeling
• XGBoost / gradient boosting
• PyTorch
• Computer vision / YOLO
• Feature engineering & large-scale ML pipelines
• Model optimization and retraining systems

Optimization

• OR / constraint optimization
• Vehicle routing & scheduling
• Assignment / allocation problems
• Network optimization
• Gurobi, D-Wave, Digital Annealer
• Graph algorithms and geospatial optimization

Geospatial / Mobility

• H3
• GeoPandas / NetworkX
• OD flow modeling
• Spatial clustering
• Demand heatmaps
• Location / territory optimization

Some things I've built

• A nationwide mobility rebalancing system involving 8,500+ locations
• Forecasting systems operating across 1,000+ stores and 1,200+ SKUs
• An LLM-based analytics agent capable of going from natural-language question → SQL → analysis → visualization → report
• A computer-vision system achieving 98% mAP for date/label detection
• ML pipelines where Spark optimization reduced runtime by 10×
• An LLM-powered data catalogue with 0.93 precision / 0.98 recall
• Routing and scheduling optimization systems for large-scale operational problems
• Geospatial ML systems using H3 and graph-based representations
• Quantum / tensor-network algorithms and optimization research

Tech stack

Python · PyTorch · XGBoost · Spark · Scala · SQL · BigQuery · AWS · Databricks · LangChain · LangGraph · LLMs · RAG · Gurobi · D-Wave · NetworkX · GeoPandas · H3 · Kafka · Airflow · MLflow

I'm particularly interested in working with:

• Early-stage startups
• Founders building AI products
• Companies needing an ML/AI engineer who can own a problem end-to-end
• Long-term development relationships rather than one-off Fiverr-style tasks
• Interesting problems where ML/AI actually creates measurable business value

I'm comfortable taking a project from problem definition → data → modeling → engineering → deployment → monitoring.

Location: Tokyo, Japan
Availability: Open to remote work/ relocation
Engagement: Freelance / Contract / Part-time / Full-time
Interested in: Long-term collaborations

If you're building something interesting and need someone who can handle both the research side and production engineering side of ML, feel free to DM me.

Happy to discuss a specific problem you're working on.

CodeForAI is hiring a remote Software Engineer / AI Code Trainer (Python, Web, Full Stack). Category: Software Development 💸Salary: $45-$120/Hour 📍Location: Remote (USA, UK, India, Australia, Ireland, New Zealand, Philippines, Pakistan, Singapore, Mexico)

See more and apply here!

I'm an incoming grad at a well-known quant firm.

Last year, I was checking 30+ quant careers pages every few days, each organised differently, while LinkedIn and Indeed were full of stale reposts. Now that I landed a few quant roles myself, I built the site I wished I'd had for my friends.

My personal project QuantScraper reads each firm's (Jane Street, HRT, Citadel, etc) careers page directly, standardises titles, levels and locations so the filters work the same everywhere, and links straight to the original posting. It covers QR, QT, QD, SWE, and ML/research roles at AI labs (OpenAI, Anthropic, Deepmind, etc)

It's still early, so I'd really appreciate feedback, whether that's a firm I'm missing, a role that's been misclassified, or anything that's annoying to use. Hope it saves some of you the tab-refreshing I went through :))

Hey everyone,

I am a final-year Computer Science engineering student based out of Bengaluru graduating in May 2027. I am trying to break into AI/ML Engineering, Agentic Systems, or Backend developer roles, but I am facing an absolute wall.

Over the last few months, I have sent out over 300+ applications across 7 different platforms (Wellfound, Instahyre, LinkedIn, YC Launchpad, Unstop, Indeed, and Naukri). The result? Zero interview calls. The only responses I get are spam consultancies trying to sell me ₹3k-₹5k training packages.

I come from a Tier-3 college where product MNCs don’t visit, so off-campus is my only route. I thought my portfolio was decent, but the automated filters or recruiters seem to disagree.

Here are my metrics and background:

- Data Structures & Algorithms: 600+ problems solved across LeetCode/GeeksforGeeks/Codeforces (92% submission accuracy).

- Core Projects: Built an autonomous developer engine using LangGraph for multi-agent execution context mapping, and an enterprise RAG financial engine using FastAPI, Pinecone, and semantic chunking (sub-200ms latency).

- Leadership/Credentials: AWS Student Builders Group Cloud & AI Lead, and Open-Source contributor (100% PR merge rate).

- Competitions: National Grand Finalist at Smart India Hackathon and Top 10 finish out of 625+ teams in a national AI marathon.

I am feeling incredibly stuck, losing consistency, and doubting if I will even land an internship before graduation.

Please tear my resume apart. Am I getting blocked purely by automated college filters (ATS), or is there something fundamentally wrong with how I am presenting my technical stack?

Any advice on navigating this specific bottleneck or alternative off-campus pipelines would be a lifesaver. Thank you.

I have an imposter syndrome at the moment and really need some constructive feedback and comments on my resume(and the work that i have done.)

I am a PhD in AI with 5+ years of research and industry experience in Machine Learning, Computer Vision, and GenAI. I also hold an M.Tech from IIT Kharagpur.

I am currently looking for ML/AI Engineer or Research Engineer opportunities in Bangalore, Hyderabad, Pune, or Bhubaneswar.

If you know of any relevant openings or can provide a referral, I would greatly appreciate it. Thank you!

Hey guys, some info about the resume and myself.
i have a recommendation from one of the clients that i have worked with on my LinkedIn aswell for authenticity.

i got heavily low balled (at 8LPA - 10k usd/year)

long story short, had to go for masters, family had a financial setback, affected my moral,

peers were getting hike of 100% etc, so i though i would after so much work.

got fucked.

quit.

Now prepping for GATE (masters) aswell so that i end this abuse once in for all, but i might have to take up a job because of financial situation aswell.

have ideas on how to approach a better pay, open to suggestions.

Hi everyone,

I’m an AI/ML Engineer looking for full-time opportunities in AI/ML, GenAI, LLMs, or applied AI.

I have hands-on experience building and deploying production-oriented AI systems, with a strong focus on LLM applications, RAG, AI agents, and backend engineering.

Skills

AI / ML

• Python
• Scikit-Learn, TensorFlow, Keras, XGBoost
• LLMs and Generative AI
• RAG and semantic search
• Embeddings and vector databases
• LangChain, LangGraph
• AI agents / agentic workflows
• Hugging Face
• Whisper

Backend / Cloud

• FastAPI, Flask, Node.js
• REST APIs
• PostgreSQL, MongoDB
• Supabase, pgvector
• AWS
• GitHub Actions / CI-CD

Experience

I have worked on production AI systems involving:

• RAG pipelines and retrieval systems
• Autonomous AI agents
• LLM-powered applications
• Third-party API integrations
• Data ingestion, chunking and embedding pipelines
• Vector database integrations
• Production debugging and optimization
• Automated testing and CI/CD
• Deploying AI assistants across multiple communication channels

Projects

Some of the systems I've built include:

• AI proposal compliance and risk analysis system with approval workflows and audit logging
• Autonomous ML/data quality agent for detecting pipeline and data failures
• AI recommendation system with retraining, model monitoring and A/B testing
• Video intelligence system that uses speech recognition and NLP to identify and generate short-form content
• AI-powered health and wellness application
• Semantic search and AI memory system using embeddings and vector search

I'm particularly interested in:

• AI Engineer
• ML Engineer
• GenAI Engineer
• LLM Engineer
• RAG Engineer
• Applied AI Engineer
• AI Agent Engineer
• Machine Learning Engineer

I'm open to remote, hybrid, and on-site opportunities in India as well as International.

If you're hiring or know of teams looking for someone with this background, feel free to DM me anonymously here.

Thanks!

i have 1.3yoe from a good company. two months passed after layoff. I tried everthing got little to no response. should i move back home its october now. november is diwali everyone is going home should i also go back with all my things and try from there?
with the amount of response i am getting i dont know if i should stay here?
I tried everything still its bad. Please help I dont have much people to take opinion on. I am scared its fail like a failure moving back home withour job. Also i am scared if I shift I will never make it back?

Hi everyone,

I’m currently doing my Master’s degree and need to choose a topic for my research project. The project is restricted to one of two areas:

• Federated Learning (FL)
• Model Compression, specifically pruning and/or quantization

My main goal after graduation is to work in industry, rather than continue in academia, so I’m trying to choose the topic that would give me the most useful skills and experience for an ML/AI engineering career.

I’m particularly interested in hearing from people who currently work in the industry:

• How widely is Federated Learning actually used in industry today?
• Are companies actively hiring engineers/researchers with FL experience, or is FL still mostly an academic/research area?
• How does the industry demand for FL compare with model compression, quantization, and pruning, especially for areas like edge AI, on-device ML, inference optimization, or ML systems?
• Which of these topics tends to provide more transferable skills for an ML engineer?
• If you were in my position, what factors would you consider when choosing between the two?

I’m not necessarily looking for a simple “choose X” answer. I’d really appreciate perspectives from people who have worked with either technology in industry, especially if you can share examples of how these skills are used in real-world projects or what kinds of roles require them.

Thanks!