AI/ML Recruiter Needed – Help Me Find Strong AI Engineers

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Recruitment, Machine Learning (ML), Talent Acquisition and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@madhurim90

I understand you’re looking for quality AI/ML talent—not volume, and that’s exactly how I work. With 10+ years in recruitment, I’ve sourced for tech roles and have exposure to AI/ML, Python, LLMs, and cloud-based systems, focusing on candidates with real production experience vs. just theoretical knowledge. ? My approach: Source via LinkedIn, GitHub, niche communities & referrals Screen for hands-on projects, real deployments, and problem-solving ability Deliver few but highly relevant candidates within 5–7 days I prioritize quality, clear communication, and long-term collaboration. Happy to start and share first shortlist quickly.

@sufyanasmat38

Hello, I understand you’re not looking for volume but for genuinely capable AI/ML engineers who have real hands-on experience, not just polished profiles or course-based knowledge. The key here is identifying people who have actually built and deployed systems. I have experience finding and screening candidates in areas like machine learning, deep learning, and LLM/GenAI. Instead of relying only on common platforms, I explore GitHub, developer communities, and targeted sourcing to find stronger talent. I usually evaluate candidates by looking at their real work, how they’ve contributed to projects, and their understanding of practical, production-level systems. I can support you by filtering and presenting only serious, high-quality candidates so your time is spent on the right people.

@qualityxenter

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@chiragardeshna

Hello there, Strong alignment with this project comes from supporting hiring for AI/ML roles by combining technical understanding with structured candidate evaluation and quality-first sourcing. Clear understanding of identifying strong engineers in ML, deep learning, LLMs, and GenAI by focusing on real project experience, production exposure, and practical problem-solving ability. Hands-on approach includes sourcing from GitHub, niche communities, and targeted outreach, along with screening based on code quality, system design thinking, and familiarity with tools like PyTorch and RAG systems. Risk is minimized by filtering out keyword-based profiles, validating real-world contributions, and shortlisting only high-signal candidates. Available to start immediately—happy to discuss hiring goals or share sample screening criteria. Recent work: https://www.freelancer.com/u/chiragardeshna Regards Chirag

@hassam945

Hey this is exactly the kind of role I was hoping to find. I am an AI/ML focused Software Engineer with ~2+ years of real-world experience, so I’m not just recruiting from the outside I actually understand what strong AI/ML engineers look like in practice. That’s a big advantage when it comes to filtering signal vs noise. --> AI roles I have worked around / can hire for: ML Engineers, Deep Learning Engineers, LLM/GenAI Engineers, Computer Vision, NLP, and RAG-based system builders. --> How I judge candidates quickly: I look beyond resumes • Real project depth (not cloned/tutorial work) • Code quality & GitHub activity • Understanding of tools like PyTorch, TensorFlow, LLMs, RAG • Ability to explain their work clearly (big signal of real experience) • Any production or deployment exposure Since I have worked in AI myself, I can quickly tell who’s legit vs who just “looks good.” I also adapt fast to new technologies and trends, which helps in evaluating modern stacks like GenAI. I genuinely care about quality over quantity I would rather bring you 3 strong candidates than 50 average ones. "If this aligns, let’s connect .I would love to work long-term and help you build a solid AI team. Your work is my first priority , here I am new but enough experience don't worry it's not just words you can evaluate me with work. I prefer to discuss first because we both need to understand first each other then if its align we start working. Thank you Waiting for your response"

@ivaylo8

Hello, I’m Ivaylo, and I understand you’re seeking a recruiter who genuinely gets AI/ML roles and can deliver a tight, quality-focused shortlist of engineers. I don’t aim for a flood of resumes; I want to surface strong candidates with production-ready experience across machine learning, deep learning, LLMs, GenAI, and related areas. I’ve recruited for AI/ML positions by validating hands-on impact, not just coursework, and I can quickly separate real builders from surface-level profiles. What I’ll bring: targeted sourcing beyond LinkedIn (GitHub, niche forums, ML/engineering communities), rigorous screening for evidence of production systems, and a pragmatic assessment of what each candidate has shipped and maintained. I tailor outreach to attract engineers solving real problems, not just ticking boxes. I’m comfortable with flexible engagement models (hourly, per hire, or performance-based) and I’m ready for a long-term partnership. Best regards, Ivaylo

@ayesha86664

Hi there! You’re looking for high-quality AI/ML candidates and the real challenge is distinguishing engineers with real production experience from those who only have course projects or theoretical knowledge. I have experience sourcing and screening AI engineers for roles involving machine learning, deep learning, and LLM/GenAI projects. I focus on finding candidates through GitHub, niche ML communities, and professional networks, and I quickly assess strength by reviewing real project contributions, code quality, and understanding of production pipelines. I will help you identify and pre-screen top AI talent, ensuring each candidate has practical experience in PyTorch, RAG systems, or other relevant frameworks, so your interviews focus only on genuinely qualified engineers. Check our work: https://www.freelancer.com/u/ayesha86664 Do you want me to focus more on full-time hires, contractors, or both for this search? I am ready to start — just say the word. Best Regards, Ayesha

@andriip30

Hi there. - What has been your biggest challenge in sourcing strong AI engineers? - How do you evaluate a candidate's practical experience versus theoretical knowledge? This project sounds interesting. My approach would be focused on sourcing candidates from niche communities and platforms like GitHub. I’ll filter for quality by screening thoroughly and assessing real-world experience over flashy qualifications. In my past roles, I've recruited for various AI positions, including machine learning specialists and data scientists. I've found that separating genuine talent from those with just a theoretical background can often be done through targeted questions and practical tests. I have a solid understanding of the technical landscape in AI, including tools like PyTorch and LLMs, so I can identify real expertise. I’m ready to help you find the right candidates quickly and effectively. Hope to discuss more on chat. Best, Andrii.

@shafeeqm6

Hi there! I see you need a recruiter who truly understands AI/ML roles, focusing on quality over quantity — exactly what you described. I’m excited to help source and screen AI engineers skilled in machine learning, deep learning, LLMs, and GenAI, ensuring candidates have real production experience, not just credentials. I’m keen to find talent beyond LinkedIn, tapping niche communities and smarter sourcing channels. Key skills for this project: • Candidate sourcing • Screening and vetting • AI/ML role understanding • Knowledge of PyTorch, LLMs, RAG • Smart sourcing beyond LinkedIn I’d love to discuss what tools you prefer for sourcing and screening. While I’m new to Freelancer, my recruiting experience is built on real-world work. Happy to chat or call anytime to explore your needs in detail. regards shafeeq

@olgierdg

You don’t want keyword matchers-you want people who’ve actually built ML systems and can prove it. That’s exactly where most recruiting breaks. Here’s how I’d handle it: -> Source from GitHub, Kaggle, research forums, and niche AI communities -> Shortlist based on real signals (repos, commits, deployed projects) -> Screen with practical questions (RAG setups, model deployment, trade-offs) -> Flag only candidates who’ve shipped, not just learned I’ve worked closely with AI engineers and built systems using LLMs, RAG, and automation workflows-so I know what "real experience" looks like vs tutorial work. Result: helped filter candidates for AI projects, cutting interview noise by ~70% and surfacing only production-level talent. Quick question—what’s the first role you want to fill (LLM, CV, NLP)?

@Georgeoustar

Hi, I can help you source and screen strong AI engineers with a quality first approach. I understand the difference between someone who has real hands on experience in ML, LLM, RAG, PyTorch, deployment, and evaluation, versus someone who mainly has courses or demo projects. My focus would be targeted sourcing, direct outreach, first pass screening, and presenting only a small number of candidates who look genuinely worth your time. I would source beyond Easy Apply using LinkedIn, GitHub, niche communities, research profiles, and technical footprints that show real build experience. For screening, I look at project depth, production ownership, model and tooling choices, problem solving clarity, and whether the candidate can explain tradeoffs instead of repeating buzzwords. I am open to hourly, per hire, or performance based collaboration, and I can start quickly if the role scope is clear. Best regards, George

@markop815

I understand you need quality over quantity when sourcing AI/ML engineers, focusing on real production experience in LLMs, PyTorch, and GenAI. I’ve worked on recruiting technical profiles where CVs looked strong but lacked real depth; I improved screening by evaluating GitHub code, system design understanding, and real deployment experience. I source from GitHub, niche communities, and targeted outreach, not just LinkedIn, and focus on candidates who have built and shipped real AI systems. I’ll help you identify truly strong engineers and build a reliable long-term pipeline.

@seandinwiddie

I specialize in sourcing and screening AI/ML engineers for roles involving machine learning, deep learning, LLMs, and generative AI. I focus on quality over quantity — identifying candidates with real production experience rather than just coursework or copied projects. I leverage LinkedIn, GitHub, niche AI communities, and direct outreach to uncover strong candidates. Screening includes verifying hands-on experience with PyTorch, LLMs/RAG, and deployed ML systems. I provide shortlists with detailed candidate assessments, so you only see the best fits. Previous work includes placing ML engineers for startups and mid-sized tech firms.

@meenthakur1100

Hello, I built & supported hiring for large scale AI and ML teams, helping founders and startups identify engineers who actually build production systems — not just course-based portfolios. I understand the difference between someone who “knows AI” and someone who has shipped real systems using PyTorch, LLMs, RAG pipelines, vector databases, and model deployment. I typically source from GitHub, research communities, Kaggle, niche AI Slack/Discord groups, and selective LinkedIn outreach — focusing on quality over volume. My screening approach is simple but effective: * Check real GitHub contributions (not forked demos) * Ask architecture-based questions (not theory-only) * Evaluate production exposure (deployment, latency, scaling) * Identify originality vs copied projects One thing I do differently: I filter candidates based on problem-solving ability and real-world AI thinking, not just resume keywords — saving you hours of interviews. A few quick questions: * What’s the first role you want to prioritize (LLM, ML, infra, full-stack AI)? * Are you hiring for startup speed or enterprise stability? * Remote only or specific time zones? * What’s the biggest hiring mistake you want to avoid? * What would make you say “this recruiter really gets it”? Looking forward to discuss your hiring needs in detail. Best regards, Meena S.

@teerthofficial

Hello there, I’ve carefully reviewed your project details and fully understand your requirements. I’m confident that I can deliver high-quality results that meet your expectations within the given timeframe. I’d be happy to discuss your project further and get started right away. Best regards, Thanks

@ranaabdullah249

Hi let's get straight to the point Where I source: LinkedIn (but I almost never use Easy Apply I do targeted outreach) GitHub (I search for real repos with production-grade code, not just notebooks) Twitter/X (AI communities, #ML, #LLM, #RAG threads) Private Slack/Discord groups (Latent Space, MLOps, EleutherAI, etc.) Referrals from the engineers I’ve already placed How I quickly judge real strength: I look for three things in the first 5–10 minutes: Production evidence — Have they shipped models that are actually running in production (latency numbers, scale, monitoring, cost optimization)? Depth of tools Can they comfortably talk about trade-offs (e.g. why they chose LangGraph over CrewAI, or why they picked Qdrant over Pinecone for their use case)? Real projects vs course projects I ask for one specific system they built end-to-end and dig into the messy parts (data quality issues, drift, cost, failures). If they can explain the hard parts clearly, they’re usually legit I’m very good at filtering out the “I did a Kaggle competition and took an LLM course” candidates quickly. Thanks

@invoketech

HELLO, I have worked on similar projects to what you are looking for, and I can exceed your expectations.I would love to work on your project. We understand you’re not looking for volume—you want genuinely strong candidates who have built real systems, not just completed courses. We have experience supporting hiring for AI/ML roles including LLM engineers, data scientists, and applied ML engineers. We focus on quality sourcing through platforms like GitHub, research communities, and niche AI forums—not just LinkedIn—so you get candidates who actually ship production-level work. Our screening approach is practical: we look at real project depth (not just buzzwords), check for hands-on experience with tools like PyTorch, TensorFlow, and LLM frameworks (RAG, fine-tuning, APIs), and validate whether they’ve worked on scalable or production systems. We also assess problem-solving ability and clarity in explaining their work Working Flow: Sourcing Strategy → Identify candidates from GitHub, niche AI communities, and targeted platforms Profile Evaluation → Review real projects, repos, and production experience (not just resumes) Initial Screening → Conduct structured calls to validate technical depth and practical knowledge Shortlisting → Share only high-quality, pre-vetted candidates with clear insights Feedback Loop → Refine search based on your feedback for continuous improvement Looking forward to working together. Invoke Tech

@SalinaSof30

Hi, I specialize in recruiting AI/ML talent and understand that quality matters far more than quantity. I focus on sourcing **engineers with real production experience** in machine learning, deep learning, LLMs, and GenAI, rather than just those with course projects or keyword matches. **Here’s how I work:** * **Roles recruited:** AI/ML Engineers, ML Ops, Deep Learning, NLP/LLM specialists * **Candidate sourcing:** LinkedIn, GitHub, Kaggle, niche AI communities, and referral networks * **Screening:** I quickly assess candidates’ credibility by reviewing their **projects, contributions, and production experience** rather than just resumes. I understand terms like PyTorch, LLMs, RAG, and what “production-ready” truly means. I prioritize **quality over quantity**, ensuring you see only **strong, vetted candidates** who can contribute from day one. I’m flexible with compensation — hourly, per hire, or performance-based. Looking forward to helping you build a world-class AI team. Best regards, Sukrati