AI Specialist & Senior Software Engineer Needed

@Microlent

Hello, Make it real! I am excited about the opportunity to work as an AI Specialist and Senior Software Engineer on this project. With over 7 years of experience in software development, specializing in AI technologies, I am well-equipped to meet the requirements outlined. My expertise lies in Python, AI/ML technologies, Docker, and Linux environments. I have a proven track record of successfully developing AI solutions and working with APIs and Git. Additionally, my strong debugging skills and experience with modern development workflows make me a perfect fit for this role. I have previously worked on AI training and model evaluation platforms, enhancing my capabilities in this area. I am confident in my ability to troubleshoot issues independently and ensure smooth functioning of the system. I am eager to discuss in more detail how I can contribute to this project. Please review my portfolio here: https://www.freelancer.com/portfolio-items/11539740-microlent-systems-portfolio and let's chat further about your project requirements. Sincerely, Rajesh

@MashoodurRehman1

Make it real. I am an experienced software engineer with over 7 years in software development and a strong focus on AI and machine learning technologies. My professional background includes extensive work with Python, AI/LLM technologies, and I have a proven track record of implementing AI solutions in Linux-based environments. I have strong experience with Docker, managing containerized applications, and routine use of shell scripting in Ubuntu/Linux. My debugging skills and problem-solving abilities are well honed, allowing me to efficiently troubleshoot and adapt to new development challenges. Additionally, I am conversant with modern development workflows involving APIs, Git, and agile processes. Though I have not worked with Outlier, DataAnnotation, Snorkel, Mercor, or Alignerr, I have substantial hands-on AI training experience with similar platforms, demonstrating my ability to quickly adapt to new tools. I am keen to explore how my skill set can meet your project needs and would welcome the opportunity to further discuss the role and its challenges. Please let me know if we could schedule a call to delve deeper into this opportunity.

@gunslinger

Make it real. Hi! I’m a senior software engineer with 10+ years of experience, with strong hands on work in python, AI/LLM systems, docker, linux and APIs. I’ve built and worked on production AI applications, including RAG, LLM integrations and model related workflows and I’m comfortable jumping into an existing codebase, debugging issues and getting things working without hand holding. I work independently and care about understanding why something is broken. The role sounds like a good match for the kind of work I actually do and I’d be happy to discuss the technical details. Kindly contact me for further discussion.

@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 Python, Linux, Software Architecture, Ubuntu, Docker, AI Model Development, AI Research, AI Development, AI Quality Assurance, AI Training Data 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.     

@DeveloperStation

Make it real Hi, On a recent contract I built a secure CI pipeline with deterministic data ingestion, Docker containers and a Linux-based workflow. The client left 5 stars and said we helped get the app to a great start. Your note about jumping into an existing environment and troubleshooting without supervision is the real work here. Quick question: is the environment already running in Docker on Ubuntu, or do you need it stood up and stabilized first? That changes where I start debugging. On the AI side, I've built LLM-backed tools with the OpenAI API, including an internal system that automates business development workflows with evidence-backed prompts and Redis queuing. Point me at the repo or a failing case and I'll trace it first. Where's the current pain? Adil

@kabirchy

Hi there, I understand you need a senior software engineer with proven AI development experience to lead your AI initiatives. With over 5 years of professional software development and extensive AI project work, I can deliver exactly what you're seeking. My approach focuses on building robust AI solutions using Python, Docker containers, and Linux environments like Ubuntu. I've successfully developed AI models from research phase through training data implementation and quality assurance testing. My software architecture background ensures scalable, maintainable AI systems that integrate seamlessly with existing infrastructure. I've worked extensively with AI model development cycles, from initial research and prototyping to production deployment and ongoing quality assurance. My experience includes managing complete AI workflows including data preparation, model training, validation, and performance optimization within containerized environments. I'm ready to tackle your AI challenges with proven technical expertise and practical solutions that drive real business results. Best Regards, Khorshed Alam, RS Software

@corpmember29

((Make it real)) I have carefully reviewed your requirements and understand that you need a hands-on senior engineer who can work independently with Python, AI/LLM technologies, Docker, Linux, APIs, and Git. I have 11+ years of software development experience with strong experience in Python-based backend systems, AI/LLM integrations, API development, Dockerized applications, debugging, and Linux environments. >>> 40-45 hours weekly I am available for work<<<< >>> you will track all progress of the project thru the tracker <<< I am comfortable working directly in Ubuntu/Linux, using shell commands, investigating existing codebases, resolving technical issues, improving performance, and delivering clean, maintainable solutions without requiring constant supervision. My experience includes AI/LLM workflows, API integrations, model-related development, data processing, automation, and production deployments using Docker and Git-based workflows. I can quickly understand your existing environment, identify issues, implement fixes, and contribute to ongoing AI/software development work. I am available to start immediately and can work independently as required. Thanks, Christina

@starttechno

Make it real — you need a senior engineer who can enter an existing Linux environment, understand the current architecture, troubleshoot independently, and turn AI/LLM-related work into reliable software. I bring 10+ years of professional development experience and have delivered 249+ projects across Python, APIs, backend systems, and production web applications. My AI experience includes ChatGPT and Claude integrations, agentic workflows, fine-tuning, prompt and response evaluation, automation, and AI quality assurance. I work comfortably with Python, FastAPI/Django, REST and GraphQL APIs, Git-based workflows, and systematic debugging of unfamiliar codebases. I can also support model-development and training-data workflows where consistency, traceability, and evaluation quality matter. For the technical environment, I use Ubuntu/Linux and shell tools daily, containerize services with Docker and Docker Compose, configure reproducible development environments, inspect logs and processes, isolate dependency issues, and add tests or diagnostics to prevent regressions. I would begin by reviewing the repository, Docker setup, environment configuration, API integrations, and current failure points, then document fixes and leave the workflow easier to maintain. Is there already a Dockerized Ubuntu environment and repository available for the initial troubleshooting? Muhammad Saad

@denisSolution

Hi, I'm Denis, a software engineer with experience building AI-related systems in Python and Linux environments. I understand you need someone who can work directly in an existing codebase, debug efficiently, and integrate AI workflows smoothly. The focus on Docker, Ubuntu, and APIs matches my recent work on systems where AI components needed to run reliably alongside other services. In similar projects, I’ve handled model evaluation pipelines and integrated third-party APIs while keeping the environment consistent across deployments. The debugging and API experience you listed would be the main tools I’d use to get up to speed quickly. I can start working right away. Let's connect and discuss the details. Thanks, Denis.

@C0RETECHIES

Make it real Hello, System understanding: Need a senior engineer to develop Python AI/LLM services on Linux, Dockerize them, integrate APIs, and work independently with Git. Experience with model-eval platforms like Outlier, DataAnnotation, Snorkel, Mercor, Alignerr is a plus. Technical approach: Create modular Python services (FastAPI) wrapped in Docker Compose. Use LangChain for LLM orchestration, store state in PostgreSQL/Redis, expose REST endpoints. Automate testing/deploy with GitHub Actions on Ubuntu. Core modules: - LLM layer: prompt handling, token management, fallbacks. - Data pipeline: API/scraping ingest, preprocessing. - Eval/training interface: hook to Outlier-style platforms, metric logging. - Docker dev env: health-checked containers, local dev scripts. - Monitoring: basic logging/alerts for stability. Relevant systems: - AI Slack Clarification Assistant (n8n, OpenAI, Whisper, LangChain) - multi-step reasoning, API glue. - AI Sales Call Automation (VAPI, GPT-4, Google Sheets) - voice AI, transcript analysis, CRM updates. - AI Chatbot Backend (n8n, GPT-4o mini, weather API) - intent detection, memory, webhooks. Implementation strategy: Wk1: Ubuntu setup, Docker, repo clone, CI pipeline. Wk2: Core LLM service + Dockerfile. Wk3: Data ingestion + eval hooks, unit tests. Wk4: API integration, load test, handover docs. Questions: 1. Which LLM providers (OpenAI, Anthropic, open-source) are prioritized? 2. Should we extend existing Docker images or start from python-slim? 3. What depth of model-eval platform integration is required (labeling, benchmarking, etc.)? Regards, Rohit

@rizwange4

As a seasoned professional in the realm of AI and software engineering, I am confident that my expertise aligns perfectly with your project's requirements. With more than 5 years in the industry, I have honed my skills across Python, AI/LLM technologies, Docker, Ubuntu/Linux, APIs, Git and more. This well-rounded familiarity with the technologies you seek is complemented by my ability to work independently in a Linux-based development environment and my strong debugging and problem-solving aptitude. In summary, I am an AI Specialist & Senior Software Engineer who can "make it real" for your project. My solid grounding in both aspects allows me to dive into existing environments proficiently and address issues promptly without constant supervision. I'm excited about the opportunity to leverage AI technologies to automate operations, improve your existing workflows and contribute to your growth journey. By choosing me to join your team, you're investing in limitless potential for your project. So why wait? Let's build together!

@toriquldev123

Hello There! I’m Md Toriqul Islam, and I’m excited to partner with you. I have 10+ years of experience in software development, with strong hands-on experience in Python, AI/LLM integrations, APIs, Docker, Linux, Git, and backend engineering. I understand you need a senior engineer who can independently work inside an existing Linux environment, troubleshoot complex issues, integrate AI/LLM functionality, and deliver reliable production-ready solutions without constant supervision. I am skilled in Python, LLM APIs, AI agents, Docker, Ubuntu/Linux, shell scripting, REST APIs, Git, debugging, and backend architecture. I’m ready to work directly in your environment, understand the existing system, identify issues quickly, and contribute reliably to the long-term development of the platform. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@vishal1145

Make it real Your AI training pipeline will bottleneck if your Docker containers aren't optimized for GPU memory allocation and your evaluation scripts lack proper error handling for model inference failures. This creates data loss during training runs and makes debugging nearly impossible at scale. Quick questions - are you running distributed training across multiple nodes, and what's your current strategy for versioning model checkpoints during evaluation cycles? Here is the architectural approach: - PYTHON & AI/LLM: Build production-grade inference APIs with proper token streaming, context window management, and fallback logic for rate-limited providers like OpenAI or Anthropic. - DOCKER & LINUX: Design multi-stage Docker builds that reduce image size by 60% and implement health checks that auto-restart containers when GPU memory leaks occur. - AI TRAINING PLATFORMS: Integrate evaluation workflows similar to Outlier's annotation pipelines where I've debugged data labeling bottlenecks that delayed model releases by weeks. I've spent 8 years building ML infrastructure for fintech and healthcare clients where downtime costs $10K per hour. Let's jump on a quick call so I can assess your current setup before proposing the exact fixes you need.

@rajat07me

Make it real — this role needs someone who can step into an existing Linux environment, debug quickly, and get blocked AI-related workflows moving again without hand-holding. That usually comes down to being strong not just in Python, but in Docker, shell-level troubleshooting, API integrations, and reading through unfamiliar code calmly. I’d approach it by first validating the environment, dependencies, container behavior, and failure points so fixes are made at the root rather than patched on the surface. My background is in hands-on software engineering across Python-based systems, API-heavy applications, and AI/LLM-related implementations where reliability matters as much as the model layer itself. I’ve worked in Ubuntu-based setups, Git-driven workflows, and debugging scenarios where the main job was to enter an existing stack, identify what is breaking, and get it stable fast. If helpful, I can also review the current Docker setup and highlight any environment or deployment risks early. Is the first priority debugging an existing AI pipeline, or building new functionality on top of it? And are you using external LLM APIs, open-source models, or a mix of both right now?

@arslanshahid386

Hi, I can help you with this project. I have relevant experience with Python, Software Architecture and can handle the work from development to testing and delivery. I've reviewed your requirements and can provide a clean, reliable, and responsive solution. Let's discuss the details and get started. Best, Arslan Shahid

@mayr81

Make it real , Senior engineer focused on AI-enabled software delivery. I build and debug Python services in Linux/Ubuntu environments, using Docker to reproduce consistent runtime behavior. I integrate with external APIs, apply modern Git-based workflows, and troubleshoot performance or failure modes by inspecting logs, dependencies, and system constraints until issues are resolved. For AI work, I’ve shipped LLM/AI features end-to-end: designing evaluation or inference flows, wiring prompts/models into application logic, and validating outputs with practical test cases. I’m comfortable working independently in an existing stack, quickly ramping up, and turning ambiguity into working code and reliable deployments. I don’t use AI tools to generate proposals or assistance here; I’ll deliver directly on engineering execution.

@ImranAli01

Make it real Hello, I got that you need a senior Python engineer who can independently troubleshoot and build AI/LLM systems in a Linux environment, with strong Docker, API, Git, and debugging experience. This is what I can help you with, let's chat. My approach is to work directly inside your Ubuntu environment, using Python, Docker, shell tooling, Git, and API integrations to diagnose issues, improve existing services, and deliver production-ready fixes without constant supervision. I’m comfortable debugging complex AI workflows, evaluating model behavior, and tracing failures across code, containers, APIs, and infrastructure. As final deliverables you will receive reliable code changes, AI/LLM development support, Dockerized environments, debugging and performance improvements, API integrations, clean Git commits, and clear technical documentation where needed. I’m comfortable working independently and can quickly adapt to an existing codebase and development workflow. One thing I'd like to confirm before we start: will the initial work focus primarily on an existing AI platform/codebase, or will new AI functionality also be developed? Let's discuss the environment and first technical priorities. Best Regards, Imran

@Aaleen110

This is a hands-on technical role, so the immediate focus is getting the AI model working with the data sources through APIs. We will use Python as the primary language, containerizing everything with Docker on Ubuntu. Debugging will be done through a combination of Python's built-in tools and shell scripts, also leveraging Git for version control. The failure point is usually around API integration, where different systems do not talk to each other as expected, so I will build out integration tests as soon as the API schemas are defined to catch these issues early. What is the expected volume of data processed daily for the AI model training? 8 reviews on here, everything delivered on time and on the agreed price so far, plus Preferred Freelancer status. I need the API documentation for the data sources.

@rosscarter1011

Make it real The line that tells me this is the right fit is the ask to jump into an existing environment and troubleshoot without much supervision, because that is exactly the kind of hands-on Python and Linux work I do rather than greenfield builds that need long ramp-up. I would start by cloning the repo, reading the Docker setup and getting the stack running locally on Ubuntu so I can reproduce whatever is broken before changing anything, then trace the issue through the logs and the API calls with standard shell tooling and a debugger. My background is Python-first across web scraping and data extraction jobs like the RacingPost scraper update and HomeDepot link extraction, where I worked inside other people's codebases on Linux, fixed failing pipelines and kept the Git workflow clean without needing hand-holding. I am comfortable with Docker for spinning up services, with shell commands for day-to-day navigation and with the modern API and Git workflows you described, and I have spent time around LLM tooling in my AI and web-scraping work. I work independently and bill against milestones if that suits the hourly arrangement. Do you want me to start on a specific open bug, or spend the first few hours mapping the environment and documenting how the pieces connect? Share the repo access and the Docker compose file and I will get the stack running and send back what I find before I change any code.