Support and Engagement Specialist - AI/LLM/GenAI

@shadabkhan92

As a passionate AI professional, I am MOHD SADAB and my agency thrives on building production-level AI infrastructure that works seamlessly within various workflows- even offline or in restricted environments. I have a strong understanding of Python and its related toolsets such as FastAPI REST APIs, Pydantic models, and various testing frameworks like pytest. Additionally, my experience with AWS and Docker containerization ensures frictionless deployment of AI models. What truly differentiates my team is our ability to orchestrate efficient Git workflows and implement SOLID principles in development, ensuring clean architecture for optimal performance. Our comprehensive familiarity with databases like SQL, NoSQL, Apache Cassandra, RDS/Aurora, DynamoDB, and Redis caching adds further value to your project. The project you are offering is a perfect match for my skills and expertise. By partnering with me, you gain an efficient problem-solver and technical communicator who can quickly locate issues using concrete evidence. Further, I am committed to sharing insights from each resolution in user-friendly language. I look forward to leveraging my tech-stack proficiency (including Hugging Face ecosystem) into this role to effortlessly support AI/LLM applications, semantic search algorithms, agent workflows while teaching advanced concepts such as RAG. Hire me so we can merge our capabilities towards successful project delivery!

@mayankinnovative

I understand you need a dedicated technical support partner for 2 hours daily to troubleshoot Python/FastAPI backends, resolve complex GenAI/RAG and agentic pipeline bottlenecks, support AWS deployments, and mentor teams with structured root-cause analysis. My approach: Apply a rigorous diagnostic workflow across logs, traces, and API responses to isolate issues between retrieval, prompts, model inference, and infrastructure. I will optimize async FastAPI endpoints, refine LangGraph/LangChain agentic chains, manage vector search pipelines, and validate fixes using pytest. I will maintain clean Git workflows, update Jira tracking, and provide clear technical documentation and mentoring tailored to your engineering team. 1. Which vector database (e.g., Pinecone, Qdrant, Milvus) and embedding models power your current RAG setup? 2. Are support queries handled primarily via asynchronous ticketing or scheduled live debugging calls? I am confident in delivering responsive, high-quality technical support to keep your AI workflows running smoothly.

@kumarashish99

Hi, I’m interested in this part-time AI/LLM technical support role. My strength is working across **AWS, Python, FastAPI, Docker, CI/CD, backend systems, and AI/GenAI infrastructure**, while troubleshooting issues across application, model, data, and deployment layers. I can support Python/FastAPI applications, APIs, RAG workflows, vector search, LLM integrations, agent workflows, AWS deployments, Git/GitHub Actions, logging, monitoring, secrets, and production troubleshooting. I’m comfortable reviewing code, logs, API responses, traces, configurations, and test results to identify the actual root cause rather than guessing. I also have hands-on exposure to **LangChain, LangGraph, embeddings, pgvector, prompt/structured outputs, tool calling, AWS Bedrock, and AI application deployment**. On the infrastructure side, I work extensively with Lambda, ECS, Docker, S3, IAM, API Gateway, CloudWatch, RDS, Redis, and Terraform. I’m comfortable explaining technical concepts to clients and guiding them through solutions rather than simply fixing issues myself. The proposed 2 hours/day engagement works well for me, and I can provide consistent technical support and documentation.

@kshubham102

Hi, I’m interested in supporting your AI/LLM projects on a part-time basis. My background combines Python development, AI/ML application development, FastAPI, LLM integrations, RAG, agentic workflows, and deployment/debugging, which fits well with the technical support and client-facing nature of this role. I can support with: Python, FastAPI, Pydantic, async APIs and pytest LangChain/LangGraph, prompt engineering, tool calling and structured outputs RAG pipelines, embeddings, vector search, reranking and retrieval debugging LLM/agent workflow debugging using logs, traces and API responses Hugging Face, model inference and fine-tuning concepts Docker, Git/GitHub Actions and deployment troubleshooting AWS services, APIs, databases and caching Jira-based issue/bug tracking and technical documentation Explaining technical concepts clearly to developers and non-technical clients My approach to support is evidence-driven: first understand the issue, isolate whether it is code, data, retrieval, model, prompt, or infrastructure, reproduce where possible, identify the root cause, and then explain the fix and next steps clearly. I’m comfortable with the ~2-3 hours/day part-time engagement and independent execution with regular client communication. Availability: Can start with a short trial/project to demonstrate my troubleshooting and communication approach. Regards, Shubham Sharma

@Cruz1217

Hi, I notice the client's issue with debugging Python and FastAPI applications, which often involves reviewing logs and API responses to isolate technical problems. My approach is to quickly understand the client's questions, locate relevant code or configurations, and provide practical resolutions. I'd start by checking the Pydantic models and async endpoints for any potential issues. I've found that a common mistake in troubleshooting LLM and GenAI applications is not properly validating hypotheses using concrete evidence, which can lead to incorrect root causes and solutions. The client can expect me to explain root causes and solutions in language tailored to their audience, and provide workarounds or next steps if a final fix cannot be deployed immediately. I'll set up a Copilot-enabled development setup to efficiently debug and resolve issues.

@indreshpnk

Hello, I understand you need part-time technical support for AI/LLM/GenAI applications, including debugging, development, deployment, and mentoring. The goal is to provide reliable issue resolution while clearly explaining technical solutions to your team. Here’s what I can provide: * Debug Python, FastAPI, LLM, RAG, agentic workflows, APIs, and data pipelines. * Support LangChain, embeddings, vector search, prompt engineering, AWS, Docker, and Git workflows. * Provide practical mentoring, testing, documentation, and root-cause analysis. I bring over 4+ years of experience in Python, backend development, REST APIs, databases, AWS, Docker, and software engineering, with hands-on experience building scalable applications and solving technical issues. Just to clarify a few things: * Which AI/LLM components are currently causing the most technical issues? * What is the current AWS and development environment setup? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@alfatrade2

A rate of INR 350-400/hour and two hours a day means the main value here is diagnostic speed, not building from scratch. And in closed or offline environments, logs and traces are often unavailable, which slows down RAG debugging a lot. I've built an outbound voice agent with offline recognition using Whisper, so restricted environments are familiar territory for me. Which combination causes problems more often right now: LangChain or custom-built agents?

@ravikumarsingh1

I’m interested in the part-time AI/LLM technical support role. My current work combines RAG/knowledge-base engineering, AI automation, AWS/cloud infrastructure, Docker, Git/CI/CD, APIs and technical client support. I’m currently working on a structured IFI/BTCX AI Knowledge Base, including document organization, RAG indexing, grounded knowledge, platform investigation and AI-ready documentation. I also have hands-on experience building AI automation workflows using n8n and OpenAI APIs, integrating external APIs and deploying services on AWS/Docker. My broader engineering experience includes AWS (EC2, S3, Lambda, IAM, CloudWatch), Docker, Kubernetes/K3s, Terraform, Jenkins/GitHub Actions, PostgreSQL, Redis, REST APIs, Linux and production troubleshooting. I’m comfortable debugging issues by tracing the problem across code, APIs, data, prompts, retrieval, configuration and infrastructure, validating the fix, and explaining the root cause clearly to technical or non-technical stakeholders. I also have experience working independently with clients and mentoring them through technical workflows and implementations. I’d be happy to support the project for the required ~2 hours/day and grow further into the deeper LLM/agentic stack as required.

@ronakkataria1

On a restricted network, LLM code often breaks before it reaches your logic. tiktoken downloads its encoding file the first time it runs, so a LangChain splitter or embedding call that counts tokens fails offline. Hugging Face checks the Hub before its local cache unless HF_HUB_OFFLINE is set, which on a blocked network means long timeouts. I pre-seed those caches and write the steps into the repo's setup notes. Your Copilot point matters too. Its LangChain and LangGraph suggestions often follow an older API than the version pinned in your lockfile, so I check the installed version and prove every fix with a test before it reaches the client. For a client question I work the way your scenario table does: prompt, retrieved chunks, model input, output, then name the layer at fault, with a workaround when the fix can't ship the same day. Then I explain it in the client's terms. Background: LLM and RAG systems in production on GPT-4, LLaMA, Qwen and Claude, LangGraph agents, AWS Lambda and API Gateway, Docker and CI/CD. I've also led and mentored a 12-person engineering team. My rating here is 5 out of 5. In the restricted setup, is the model served locally, say through vLLM or Ollama, or reached through a private cloud endpoint? Ronak

@tamannarajput18

Hi, I’m interested in supporting your AI/LLM technical support project. I can help troubleshoot and resolve issues across Python, FastAPI, LLMs, RAG pipelines, agentic workflows, APIs, databases, and cloud deployments. I can assist with: • Python, FastAPI, Pydantic & API debugging • LangChain, LangGraph, RAG & vector search • LLM integration, prompting & tool calling • AWS, Docker, CI/CD & deployment issues • SQL/NoSQL, Redis & data pipelines • Testing with pytest and technical documentation • Debugging logs, traces, API responses and production issues I follow a practical approach: understand the issue, identify the root cause, validate the solution, and clearly explain the fix to the client team. I’m also comfortable with technical communication and explaining AI/LLM concepts in a simple, understandable way. I’d be happy to discuss the project and start with the required part-time support. Thanks!

@abdurrehman373

I have extensive experience working with the Python ecosystem, specifically building and maintaining FastAPI backends, RAG pipelines, and agentic workflows using LangChain. I am comfortable troubleshooting complex AI applications, from debugging LLM inference and vector search retrieval to managing deployment lifecycles on AWS. My approach to technical support is rooted in systematic debugging—isolating issues across code, model behavior, or infrastructure—and providing clear, actionable explanations that bridge the gap between technical complexity and project goals. I am well-versed in maintaining clean, version-controlled codebases using Git and streamlining delivery via CI/CD pipelines. I am available for the requested daily 2-hour commitment and can effectively manage tasks and documentation within your existing Jira workflow. I have experience across the full stack of modern AI engineering, including: - Backend: FastAPI, Pydantic, and asynchronous operations. - AI/LLM: LangChain, LangGraph, prompt engineering, and structured tool calling. - RAG: Ingestion pipelines, hybrid retrieval, and reranking strategies. - Infrastructure: AWS, Docker, and logging/monitoring via CloudWatch. If you have a specific technical block or a set of documentation you'd like me to review first to align with your current standards, feel free to share the details.

@iFixNow

Hi, there With only two hours a day, the real risk is slow answers and fixes nobody remembers. Most "the LLM is wrong" tickets turn out to be retrieval: bad chunking, a changed embedding model or a stale index. Agent code also broke widely when LangChain and LangGraph hit 1.0 and create_react_agent gave way to create_agent. In offline setups, pip installs and Hugging Face downloads fail silently unless models and wheels are cached locally. As CTO of Assist A Boss I built a FastAPI and LangGraph support assistant on AWS Lambda with hybrid search and reranking. When staff said it made things up, CloudWatch traces showed the model was fine and chunks were cutting tables in half. Fixing chunking and adding a 150-question pytest eval set lifted answer accuracy from 61% to 88%, and I taught the team to read traces themselves. As Tier 3 Migration Specialist for Leidos I was the escalation point for 2,500+ users, logging root causes in ServiceNow so the help desk solved repeats alone. - Clarify first, then trace to code, data, retrieval, prompt or infra - Prove each fix with logs or a pytest case - Log cause, fix and workaround in Jira - Explain it plainly so your team learns If you're looking for scalable, practical value rather than just a one-time project, I can help you achieve meaningful results through small but lasting improvements that will exceed your expectations and surprise everyone involved. William Berry

@abhilashr53

You need a hands-on AI/LLM engineer who can jump into live issues, explain complex concepts clearly, and keep client teams unblocked daily. My plan: triage incoming issues within the hour, debug across your Python/FastAPI/LangChain/RAG stack using logs and traces, resolve or escalate with documented fixes, and run short mentorship sessions as needed. I work comfortably in both cloud and air-gapped Copilot environments. My background covers LangChain, LangGraph, RAG pipelines, vector search, Hugging Face inference, AWS deployments, and FastAPI production apps. I have explained these concepts to non-technical stakeholders and junior developers repeatedly, so the teaching side is natural, not forced. Available 2 hours daily, structured around your team's active window. Can start immediately. One question: are the client teams primarily facing deployment/infrastructure issues, or is most of the friction on the LLM/RAG application logic side?

@GenomyR

Hello, For integrating FastAPI capabilities, keeping latency low and handling API timeouts cleanly is essential for a reliable user experience. I will set up the pipeline with structured schema validation, proper prompt boundaries, and streaming responses so the interface feels instantaneous. I can deliver the working integration on a test branch within 2 to 3 business days. Which model provider (OpenAI, Claude, or local) are you planning to connect with? Best regards, Tumisang

@Jeeldhola01

Hi, this role matches my daily technical work closely. I work across Python, FastAPI, RAG pipelines, agentic workflows, and AWS deployments as part of building enterprise AI systems. Fit against your stack: 1. Python/FastAPI: comfortable with Pydantic models, async endpoints, middleware, and pytest-driven testing 2. LLM/GenAI/Agentic AI: hands-on with LangChain, LangGraph, prompt engineering, and structured tool-calling patterns 3. RAG & search: ingestion pipelines, chunking, embeddings, hybrid retrieval, and agentic RAG patterns 4. ML/DL: PyTorch, transformer internals, inference optimization, and PEFT/LoRA concepts 5. AWS: Lambda, S3, API Gateway, Docker, CloudWatch, and CI/CD via GitHub Actions Approach to client-facing debugging: clarify the actual symptom before touching code, isolate whether it traces to data, model behavior, retrieval, prompts, or infrastructure, validate with logs and traces before proposing a fix, then explain the root cause and next steps in language matched to the audience. Comfortable mentoring teams on RAG and agentic AI fundamentals alongside hands-on debugging. Available for the more than 2 hours/day commitment and ready to start.

@OWAIS14325

As an AI/LLM/GenAI specialist, I bring a wealth of experience and expertise that align closely with your project's goals and responsibilities. Combining my proficiency in cutting-edge technologies such as LangChain, LangGraph, and Hugging Face ecosystem with my solid understanding of backend architectures like FastAPI and Python, I offer a unique skill set that spans across the entire development process. From prompt engineering to deploying RAG systems, I'm well-versed in every aspect of your required technical stack. My competencies in AWS- including model-serving concepts- will help ensure seamless deployment. Plus, my ability to mentor clients would be valuable in enhancing your teams' understanding of important topics like LLM fundamentals and advanced concepts. What sets me apart is my deep commitment to problem-solving and customer satisfaction. I emphasize understanding the core issues comprehensively before providing practical solutions tailor-made for the specific context - a workflow that aligns perfectly with your project's requirements. My solid track record not just in resolving client queries but also mentoring teams on root cause analysis will prove invaluable in executing this project successfully. So, let's join hands to leverage the power of AI/LLM/GenAI together!

@adiityaaa09

Hey I am familiar with python LLm,and can also help you make your project. I know many technical fields please give it a try!

@morne01

?IF YOU'RE NOT HAPPY, YOU DON'T PAY.? Recently, I helped a client streamline their AI application support, enhancing their team's efficiency and boosting their confidence in handling technical challenges. I can assist you in providing effective support for your AI/LLM/GenAI applications, ensuring seamless issue resolution and mentorship for your team. Your need for a clean and professional approach, along with strong client communication and technical teaching abilities, aligns perfectly with my skills. I have extensive experience outside of freelancer, but am new to Freelancer. My expertise in Python, FastAPI, LLMs, and AWS deployment ensures I can tackle complex technical issues and mentor your team effectively. Regards, MORNE.

@anjanat13

Hi, I’m interested in the Project Technical Support – AI/LLM Engineering role. I have experience working with Python, AI/LLM integrations, backend APIs, AI chatbots, AI workflows and technical troubleshooting, and I’m comfortable investigating issues from the code and API layer through to the AI workflow. I can support your team with: • Python backend and API troubleshooting • FastAPI and REST API integrations • LLM integrations, prompt engineering and structured outputs • RAG, embeddings, semantic search and retrieval workflows • Agentic AI, tool calling and workflow debugging • API/data-pipeline issue investigation • Git/GitHub and deployment support • Testing, debugging and technical documentation • AI application maintenance and improvements My troubleshooting approach is systematic: I first understand the issue, inspect the relevant code, logs, configuration, API responses or AI outputs, identify the root cause, validate the fix, and clearly explain the solution. I’m also comfortable explaining technical concepts in simple language, which is important for the client-facing and mentoring aspect of this role. I can commit to the required ~2 hours per day and work independently while providing clear progress updates. I’d be happy to discuss your current AI/LLM architecture and the type of technical support you need. Best regards, Anjana