Senior AI Engineer

@abhilashtv

Hi Senior AI/ML Engineer with 10+ years of experience delivering scalable, enterprise-grade AI and data solutions. Strong expertise in machine learning, generative AI, LLMs, RAG architectures, vector databases, and agentic AI workflows, combined with end-to-end data engineering (ETL/ELT pipelines, large-scale data processing). Proven track record of building and deploying production-ready AI platforms on AWS and Azure using Python, MLOps, and cloud-native architectures, with strong communication skills and effective collaboration across engineering, data, and product teams. Key Projects AI-Driven Healthcare Document Platform: Built HIPAA-compliant AI backends for secure medical document ingestion, OCR, classification, and agentic AI workflows AI-Powered Surveillance & Validation System: Developed microservices integrating YOLOv7 vision models, RAG-based AI querying, and event-driven workflows on AWS Manufacturing Spot-Weld Inspection System: Led backend AI development for real-time defect detection and optimized edge inference on Raspberry Pi Enterprise Financial Data Automation (SAP & Excel): Built AI-assisted pipelines to normalize General Ledger data, standardize Charts of Accounts, and automate Adjusted EBITDA analysis Deployed scalable, cloud-native AI services on AWS & Azure using Python, FastAPI, Django, Docker, and Kubernetes Thanks and regards

@avinashdubeyg209

Noticed you're searching for someone to spearhead diverse AI projects, including agentic interview workflows and multimodal systems. As someone who just built a RAG pipeline for a fintech client last month, navigating complex AI landscapes is second nature. Curious about the scale of your recommendation engines: are we talking thousands or millions of interactions daily? Can start today, ensuring your AI initiatives move seamlessly from concept to production. Let me know if you'd like to discuss strategies.

@vishal1145

Your multimodal AI pipeline will fail in production if you don't implement proper fallback logic when vision models timeout or return low-confidence scores. I've seen this break interview assessment systems when candidates upload corrupted images or non-standard file formats. Before architecting the solution, I need clarity on two things: What's your current inference latency budget for the agentic interview workflows - are we optimizing for sub-500ms responses or can we batch process? And what's your model versioning strategy - are you planning A/B tests between fine-tuned adapters or running multiple LLM providers in parallel? Here's the architectural approach: - LLMS + PROMPT ENGINEERING: Build a prompt orchestration layer with fallback chains (GPT-4 → Claude → Llama 3) and structured output validation using Pydantic to prevent hallucinations in assessment scoring. - MLOPS + MODEL MONITORING: Implement Weights & Biases for experiment tracking and Evidently AI for drift detection, with automated retraining triggers when F1 scores drop below 0.85. - MICROSERVICES + PYTHON: Design event-driven inference services using FastAPI and Celery with Redis queues to handle 1K concurrent interview sessions without blocking. - MULTIMODAL LLM: Build a unified embedding pipeline that processes video transcripts, facial expression analysis, and code submissions into a single vector space for holistic candidate evaluation. - RAG + VECTOR DATABASES: Set up Pinecone with hybrid search (dense + sparse) and implement citation tracking so assessments reference specific interview moments with timestamps. I've built similar AI assessment platforms for 2 EdTech companies that scaled from 500 to 50K monthly evaluations. I'm based in Pune and open to working from Magarpatta City as a consultant engineer. Let's schedule a technical call to discuss your training data quality and edge case handling before I commit to timelines.

@alizain721

Needing one senior AI owner to take ambiguous workstreams from prototype to production is exactly the gap most teams struggle with. I enjoy being that single owner who ships models, infra, and monitoring end to end. The best thing about me is I’ve worked on a very similar project recently: as Lead AI Engineer for Skallix and Program Pro I built LLM-driven coaching assistants, RAG pipelines, recommendation/ranking modules, and production MLOps for inference, monitoring, and CI/CD. I handled fine-tuning, adapters, vector DBs, prompt flows, and deployment orchestration. I understand the expected user flow (ingest -> retrieval -> model -> post-process -> evaluation), the system architecture (modular microservices, vector DB, GPU inference cluster, observability), and key features like agentic interview orchestration, assessment pipelines, and model monitoring. Tech stack I use: Python, PyTorch, Transformers, LangChain, FAISS/Milvus, Docker, Kubernetes, AWS/GCP, MLflow. I can save weeks by applying patterns I’ve already productionized. Quick questions: do you have existing infra or preferred vector DBs? What latency and availability targets matter most? Happy to chat or hop on a quick call. Regards Ali Zain!!

@AhmadXpert2410

Hi there, I understand you’re looking for a senior AI professional capable of independently owning and executing end-to-end AI workstreams across LLMs, recommendation systems, agentic workflows, multimodal AI, and MLOps. I have 5+ years of hands-on experience in AI/ML, building production-ready systems from architecture and prototyping through deployment, monitoring, and lifecycle management. My work spans LLM fine-tuning, prompt engineering, RAG workflows, vector databases, multimodal pipelines, classical ML, deep learning, NLP, CV, and applied ML, all with strong Python expertise and frameworks like PyTorch and TensorFlow. I specialize in designing scalable AI pipelines, building recommendation and ranking engines, and creating AI-driven assessment and evaluation systems. I also have experience developing agentic workflows, reusable AI components, and CI/CD pipelines for model deployment, monitoring, and optimization on cloud platforms with containers and microservices. For past projects, I have led end-to-end LLM applications, RAG-enabled knowledge systems, multimodal AI experiments, and AI evaluation frameworks, always taking ownership from initial design to production. I am open to working on-site in Magarpatta City, Pune, and flexible across engagement types, including freelance, contractor, consultant engineer, or full-time, depending on your needs. Regards, Ahmad

@Cmonosoft

Hello there, I am interested in this role as it aligns well with my experience building end to end AI systems that move from early prototypes to reliable production deployments. I have over five years of experience working across the AI and machine learning stack using Python with frameworks such as PyTorch and TensorFlow. My work has included developing LLM driven applications, retrieval based systems, recommendation engines, and AI powered workflow automation. For LLM and SLM systems, I have worked with prompt engineering, retrieval augmented generation pipelines, vector databases, fine tuning strategies, and efficient inference pipelines. Looking forward to learning more about your AI workstreams! Thanks Saurabh

@johibkhan

Hi, With over 15 years of hands-on experience in software application development and a specialized focus on algorithmic trading, AI-driven systems, and market regime detection, I am the ideal candidate to take on your Senior AI Engineer role. My proficiency in Python is a key asset for the application and development of modern AI frameworks such as PyTorch, TensorFlow, and JAX which are vital for building end-to-end AI systems capable of addressing the diverse workstreams you have outlined. In addition to my technical competence, I also bring a wealth of knowledge in MLOps and managing high-performance systems with complex real-time workloads. Throughout my career, I have designed and implemented robust, scalable architectures while keeping practicality and scalability in mind. This expertise will unquestionably serve me well in creating reusable tools and components, as well as ensuring deployment reliability, reproducibility, CI/CD practices, and model monitoring for your project.

@jaroslavcaprata

✅✅✅✅✅ Top Rated AI Engineer & ML Architect ✅✅✅✅✅ Hello, I will own and deliver end-to-end AI systems across LLMs, recommendation engines, and agent workflows using Python, PyTorch/TensorFlow, and scalable microservices, ensuring production-ready pipelines with <200ms inference latency and 95%+ model accuracy where applicable. I will design RAG systems, fine-tune models, and build evaluation pipelines with monitoring and CI/CD, improving model reliability by ~30%. I have delivered 11+ AI systems spanning LLMs, NLP, and applied ML with strong production performance. I will handle full lifecycle development—from prototyping to deployment—covering prompt engineering, vector databases, multimodal pipelines, and MLOps with reproducible training and monitoring frameworks. My approach includes building reusable AI components, optimizing GPU workloads, and ensuring scalable deployment on cloud infrastructure with >99% uptime. Previous projects include AI agents, ranking systems, and assessment engines with measurable improvements in automation efficiency (~40%). I bring strong ownership, problem-solving, and clear communication, working independently across multiple workstreams while aligning with business goals. I am open to flexible engagement models and can collaborate effectively in structured environments. If this sounds good, connect in chat and we can start. Thanks, Jaroslav Caprata

@Web1devloper

With my extensive experience in Data Analytics and Science, I can bring a wealth of expertise to the table that uniquely qualifies me for the Senior AI Engineer role. Over the course of my 8+ years in the industry, I've developed a profound understanding of applying ML to real-world problems, storing, processing, and analyzing large-scale datasets, all crucial aspects to bring your projects to fruition. My adaptability is evident in my wide skill set - from **Python** (your preferred language) and contemporary AI/ML platforms like **PyTorch and TensorFlow**, to NLP, CV, and MLOps –I’m comfortable exploring every area of modern AI engineering. Having worked with various businesses including finance, healthcare, e-commerce, and SaaS industries aligns perfectly with your cross-industry AI requirements. It helped me foster good (business & technical) communication skills as well as insights extraction strategies that are imperative for successful project management. Let's discuss how we can optimize your operations on an end-to-end basis ensuring deployment reliability and reproducibility using CI/CD practices and model monitoring. I assure you - I'm fully equipped to handle whatever comes our way and ensure the project's success.

@UzeeTech

As an experienced full-stack developer well-versed in both front-end and back-end frameworks, I believe I'm a strong fit for the Senior AI Engineer role you're looking to fill. With a seven-year stint, I've built production-grade web apps, mobile apps, SaaS platforms, and AI-powered solutions—experiences that give me a competitive edge needed to handle multiple workstreams effortlessly. In addition, my knowledge of end-to-end AI systems— encompassing training, inference, monitoring, and lifecycle management—makes me the right candidate to own and execute independent AI workstreams without much guidance. Furthermore, I'm well-adept in Python—the powerhouse of AI/ML—and have a strong command over AI/ML frameworks like PyTorch and TensorFlow. My expertise also spans across NLP,CV,and MLOps—which i can apply effectively to tackle problems relating to LLMs/SLMs,recommendation engines, agentic interview workflows,result assessments,multimodal systems et al. In terms of backend skills using Node.js / Express or Python.I can be asset for the team as I am quite familiar with APIs,microservices,cloud platforms & containers.I understand how important it is to bring balance between productivity and reliability which also deepens the conviction that I am your prime choice here. Let's not wait. Grab this opportunity and let's make some magic together at Magarpatta City,Pune.

@TeamGTW

HI, I’VE REVIEWED YOUR SENIOR AI ENGINEER REQUIREMENT AND HAVE BUILT MULTIPLE END-TO-END AI SYSTEMS ACROSS LLMs, RECOMMENDATION ENGINES, AND PRODUCTION ML PIPELINES. I CAN DELIVER HIGH-QUALITY SOLUTIONS AND AM READY TO START IMMEDIATELY. I understand you need a hands-on AI generalist who can design, build, and deploy complete AI systems—from LLM applications and recommendation engines to agent workflows and scalable production pipelines. The role requires strong ownership to convert complex problems into reliable, production-ready AI solutions. I have 7 years of experience in AI/ML, working with Python, PyTorch, TensorFlow, and modern LLM stacks, including fine-tuning, RAG pipelines, vector databases, and MLOps workflows, delivering scalable AI systems with strong performance, monitoring, and reliability. I specialize in building end-to-end AI systems, LLM applications, and scalable ML infrastructure. Let’s start a chat and discuss how I can contribute to your AI workstreams and align with your team setup. FEEL FREE TO MESSAGE ME ANYTIME. I’M USUALLY AVAILABLE AND RESPOND WITHIN 5 MINUTES. Best regards, Nikita Gupta

@ivicab6

As a seasoned full-stack engineer, I found myself well-equipped to tackle complex and intricate projects like the one you've outlined. My experience encompasses 6+ years of working across various domains, predominantly in building robust web applications from end-to-end. This has familiarized me with the ins and outs of creating systems that are not only functional but maintainable too. My knack for clean architecture will undoubtedly prove valuable as I tackle every step of the AI development cycle from requirements to deployment in your project. In addition to my expertise in novel front-end and back-end frameworks, I pride myself on my ability to automate business processes and workflows. This knowledge is aligned with your requirement for a resource capable of managing ambiguous problem statements and transforming them into scalable solutions - something I thrive on. While Machine Learning is not my primary focus, it's an added skill that comes in handy when products need analytics or reporting, which is a strong suit for me as well! Ultimately, my broad skillset combined with my holistic understanding of how intelligent systems work will enable me to realize all facets of your AI project effectively. From designing innovative recommendation engines and agentic interview workflows to creating multi-modal AI systems, I'm eager to elevate your team's capabilities through hard work, dedication, and results-oriented outcomes. Let's connect soon!

@SkillCrafts

Hello. I’ve spent years bridging the gap between experimental AI and production, mirroring the multi-project ownership you require. Recently, I architected a multi-agent RAG system that cut processing time by 70%, managing the entire lifecycle from ingestion to deployment. My experience across NLP and predictive modeling ensures that whether we are optimizing LLM inference or building custom pipelines, the focus remains on scalability and direct business value. I prioritize a modular architecture to ensure rapid iteration across multiple initiatives without technical debt. I utilize LangChain for orchestration, paired with Pinecone for vector retrieval and FastAPI for robust delivery. For specialized tasks, I implement PEFT/LoRA fine-tuning to maximize accuracy while controlling compute costs. Every solution is containerized via Docker and integrated into CI/CD pipelines, allowing me to independently own the infrastructure while maintaining seamless deployment and system stability. To align our first steps, are you prioritizing RAG-based knowledge retrieval or is the focus on fine-tuning foundational models for domain-specific logic? I’d also like to know if your preferred stack includes AWS SageMaker or a different cloud ecosystem to ensure my plan integrates perfectly with your existing tools. I’m available for a brief chat to discuss how I can take these projects off your plate and move them into production efficiently. Looking forward to hearing about your technical vision.

@workhitesh

Hey, Expert here!, I am having 4+ years of experience in Python, Machine Learning (ML), Deep Learning, Microservices, Prompt Engineering, Natural Language Processing, Model Monitoring, MLOps, Multimodal Large Language Model, Large Language Models (LLMs) and related technologies.. Waiting for your reply! would love to discuss this project with you! Open for negotiations and free technical consultation!

@aviformat

Hello, I’m a Senior AI Engineer with **7+ years of experience** building and deploying end-to-end AI systems across multiple domains. I’ve worked across diverse project streams, delivering solutions from problem definition to production at scale. My expertise spans: * **NLP & LLMs:** RAG pipelines, prompt engineering, fine-tuning (LoRA/QLoRA), evaluation systems * **Agentic AI:** Multi-agent workflows, tool orchestration, explainable AI systems * **Audio AI:** ASR (Whisper), streaming pipelines, audio captioning, multimodal audio-text systems * **Computer Vision:** Deep learning-based image processing and multimodal integration * **Recommendation Systems:** Embedding-based ranking, similarity search, ANN pipelines * **Classical ML & MLOps:** End-to-end pipelines, deployment, monitoring, CI/CD I specialize in **owning complete AI lifecycles**—architecture design, rapid prototyping, optimization, and production deployment using Python, PyTorch, APIs, and containerized systems. Recently, I’ve built LLM-powered systems, agentic frameworks, and multimodal pipelines integrating audio, text, and vision, along with scalable inference and evaluation layers. I bring strong ownership, structured problem-solving, and the ability to turn ambiguous requirements into reliable, production-ready AI systems. Happy to discuss your use cases and propose concrete solutions. Best regards, Ayush

@indreshpnk

Hello, I’m an AI generalist with 5+ years of hands-on experience building end-to-end AI systems—from LLM/RAG pipelines and agent workflows to recommendation engines and production ML systems. I’ve led architecture, fine-tuning, evaluation pipelines, and deployment (Python, PyTorch, FastAPI, vector DBs, Docker, cloud). My work includes AI interview systems, scoring engines, and automation agents with strong guardrails and reliability. I take ownership from ambiguous problem → scalable solution. Open to contractor/consulting roles and available for Pune (Magarpatta) if required.

@hoanj

Hi! I’ve handled projects where existing codebases had critical issues that needed fast, reliable fixes without breaking functionality. In your case, identifying and resolving bugs, optimizing performance, and ensuring system stability are key. I can quickly analyze the current setup, pinpoint problems, and implement clean, maintainable solutions. With extensive experience in debugging, backend and frontend integrations, and deploying stable systems, I’ll make sure everything works seamlessly. The result will be a fully functional, stable system that performs efficiently and is easier to maintain going forward. I’m ready to start immediately and keep communication clear throughout.

@iconicdev4

Hello, This role aligns well with my experience building end-to-end AI systems including LLM applications, RAG pipelines, recommendation systems, AI automation workflows, and production ML deployments. I work across the full AI lifecycle — from architecture and prototyping to optimization, deployment, and monitoring. My stack includes Python, PyTorch/TensorFlow, vector databases, prompt engineering, fine-tuning, APIs, microservices, and cloud deployment. I have built reusable AI components, recommendation models, and deployed ML systems as production APIs with monitoring and logging. I am comfortable working as an AI generalist handling multiple AI workstreams independently. Could you please clarify: 1. Primary AI use cases for this role? 2. Current AI/ML tech stack? 3. Is remote work possible or is Pune on-site required? I am open to Freelancer or Consultant engagement. Best regards.

@guptakishan805

Hello, I have 5+ years of experience as an AI/ML engineer, building end-to-end intelligent systems across LLMs, recommendation engines, and production-grade AI pipelines. I specialize in taking ambiguous problems and converting them into scalable, deployable AI solutions. My experience includes developing RAG-based LLM systems, fine-tuning models, building recommendation/matching engines, and designing agentic workflows with strong MLOps practices (CI/CD, monitoring, reproducibility). I’ve worked with Python, PyTorch, TensorFlow, vector databases, and microservices-based architectures for real-world deployments. I focus on creating reusable AI components and optimizing inference for production environments. Tech Stack: Python, FastAPI, PyTorch, LangChain, Docker, PostgreSQL Let’s connect to build scalable AI systems across your workstreams.