Production GenAI Platform on AWS

@softsolution2000

With my expertise in Java, Python, Amazon Web Services, Elasticsearch, and AWS Lambda, I am well-equipped to handle the Production GenAI Platform on AWS project. My experience aligns perfectly with the requirements you have outlined. I am confident in my ability to extend Bedrock integrations, design fault-tolerant ETL jobs, and optimize vector search strategies. I am eager to start immediately and discuss the project scope further to ensure we stay within budget. Please review my profile showcasing 15 years of experience. Your satisfaction is my priority, and I am ready to showcase my commitment to this project. Looking forward to hearing from you.

@kamran2012

Good to see this project, I will work across your full stack — extending Bedrock integrations with Claude 3, Titan, Llama 3, and Mistral, maintaining the LangChain and LlamaIndex orchestration layer, designing fault-tolerant Glue ETL jobs optimized for Athena, tuning vector search for latency and relevance, and hardening the API Gateway layer with auth and rate limiting. Available on IST and ready to start immediately. One area I will focus on early is your retrieval pipeline tuning. Most Bedrock RAG setups lose relevance because they use a single embedding model and fixed chunk size across all document types. I will set up an evaluation harness that benchmarks retrieval precision across different chunking strategies and embedding models (Titan vs Cohere) so we can tune based on measured results rather than assumptions — this directly impacts answer quality in production. Questions: 1) Which vector database are you using — OpenSearch, Pinecone, or Bedrock's native knowledge base? 2) What is the current bottleneck you are hitting in moving from PoC to production — latency, accuracy, scale, or reliability? Looking forward to discussing further. Best regards, Kamran

@avinashdubeyg209

Hi there, spotted your focus on Amazon Bedrock and the integration of Claude 3, Titan, Llama 3, and Mistral. Recently, I transitioned a Generative AI system using these exact models into production, tackling orchestration challenges with LangChain and LlamaIndex. One key learning was optimizing model selection in real-time based on workload needs. How are you handling dynamic model allocation in your current setup? Let me know if you're interested in a deeper dive into strategies; can start today.

@kumarashish99

With my extensive experience and deep understanding of AWS, particularly in relation to data engineering and machine learning, I am confident that I can bring immense value and expertise to your project. Having solved real-world problems involving the specific AWS stack mentioned in your project description, namely Claude 3, Titan, Llama 3 and Mistral, outfitted with my familiarity with Bedrock, LangChain, LlamaIndex , my integration on this production GenAI platform will be smooth and efficient. In conclusion, I believe my track record aligns seamlessly with your project requirements for productionizing Generative AI system given my experience with similar technologies on AWS stack. My proven skills in building scalable architectures tailored for big data along with an innovative mindset to tackle complex challenges squarely head on will make me a uniquely successful fit for your project. I’m thrilled at the opportunity to contribute immediately to your team and under IST time zone. Let me bring maximum efficiency to the system's performance through fine-tuning vector search mechanism or toughening up the entire API layer for better external consumption with authentication and rate limits. If hired, rest assured, I take start dates upon onboarding very seriously and moving quickly to produce final results.

@GameOfWords

As a seasoned AWS developer with a strong focus on data engineering and effective API integration, I believe I’m the ideal candidate for this role. I bring over 5 years of experience working extensively with key tools in the AWS ecosystem, including Cloud 3, Titan, Llama 3, and Mistral. Pairing this deep knowledge with your requirement of integrating LangChain, LlamaIndex and Bedrock Agents complements my list of skills. Operating efficiently within Python's modern version (3.9+), I am adept in coordinating multi-functional components such as the use of API Gateway, Lambda and Step Functions for seamless execution. I have built ETL jobs on Glue optimized specifically for Athena queries ensuring structured and non-structured data is handled effectively.

@KarthikTechCon

Hi, I’m Karthik, a GenAI & Cloud Architect with 15+ years of experience building production-grade AI/data platforms on AWS. I’ve taken multiple POCs into scalable systems using Amazon Bedrock with models like Claude, Llama, and Mistral, integrated into real-time enterprise workflows. **Relevant Expertise:** • Orchestration with LangChain + LlamaIndex + Bedrock Agents • Python (3.9+) microservices via API Gateway, Lambda, Step Functions • Vector DB design (FAISS/OpenSearch/Pinecone) for low-latency RAG pipelines • Serverless ETL using Glue + Athena for structured/unstructured ingestion • Secure API design (auth, throttling, observability, retries) **What I’ll deliver:** • Robust Bedrock integrations & optimized embedding pipelines • Fault-tolerant, cost-efficient ETL workflows tuned for Athena • High-performance retrieval strategies (hybrid search, reranking) • Production-grade API layer with monitoring & scaling • Clean, well-tested Python code + clear documentation I’ve built agentic AI systems with memory, tool usage, and autonomous workflows—aligned closely with your requirements. Comfortable working IST and available for long-term engagement. I can share concise case studies of similar GenAI platforms on request. Ready to start immediately. Let’s discuss your architecture and next steps. Warm Regards, Karthik Resonite Technologies

@vishal1145

Your Bedrock embeddings pipeline will bottleneck at scale if you're calling the model synchronously for every document chunk - I've seen latency spike from 800ms to 12 seconds once ingestion hits 10K documents per hour. The fix is batching requests through SQS with dead-letter queues and implementing exponential backoff for Bedrock throttling errors. Before architecting the production hardening, I need clarity on two things: What's your current token throughput per minute across all models, and are you hitting Bedrock's default quota limits during peak loads? Second, what's your vector database - are you using OpenSearch Serverless, Pinecone, or a self-managed solution, and what's the current p95 retrieval latency? Here's the production approach: - BEDROCK + LANGCHAIN: Implement async invocation patterns with boto3 aioboto3, add circuit breakers for model failures, and build a fallback chain that switches from Claude to Mistral when rate limits hit. - STEP FUNCTIONS + LAMBDA: Design a state machine that handles partial ETL failures without reprocessing the entire dataset, using DynamoDB for idempotency tracking and S3 event triggers for incremental loads. - GLUE + ATHENA: Partition raw data by ingestion timestamp and entity type, convert to Parquet with Snappy compression, and implement predicate pushdown to cut query costs by 60-70%. - VECTOR SEARCH TUNING: Benchmark HNSW vs IVF indexing strategies, implement hybrid search combining dense embeddings with BM25 keyword matching, and add result re-ranking using cross-encoders for the top 20 candidates. - API GATEWAY HARDENING: Set up usage plans with burst limits, implement JWT validation through Lambda authorizers, and add request/response transformation to sanitize PII before it hits the models. I've built three production GenAI systems on Bedrock in the past 18 months, including one that processes 2M documents daily for a legal tech client. I don't take on projects where the scaling strategy isn't clear upfront - let's schedule a 20-minute technical call to walk through your current architecture and identify the highest-risk failure points before we commit to the build.

@hr157

Hi, I’m a senior full-stack engineer with 10+ years of experience, including building and scaling production-grade AI systems on AWS with LLM orchestration and data pipelines. Relevant experience: Built LLM systems using Bedrock (Claude, Titan, open models) with LangChain + LlamaIndex Designed RAG pipelines with vector DB tuning (chunking, embeddings, hybrid search) Deployed serverless architectures (API Gateway + Lambda + Step Functions) Implemented ETL pipelines (Glue + Athena) for structured/unstructured data Hardened APIs with auth, rate limiting, and monitoring What I’ll contribute: Optimize Bedrock integrations (latency, prompt efficiency, cost control) Improve retrieval accuracy (embedding strategy, re-ranking, caching) Design fault-tolerant Glue pipelines for scalable ingestion Strengthen API layer (security, throttling, observability) Clean, testable Python code with clear documentation Approach: Treat LLM as a system (not just API calls): evals, tracing, fallback strategies Balance quality vs latency vs cost using measurable benchmarks Introduce monitoring (logs, traces, token usage, failure handling) Why I’m a strong fit: Hands-on with LangChain + LlamaIndex + AWS serverless stack Strong production mindset (resilience, scaling, debugging) Comfortable working IST hours and long-term ownership Let's chat!

@chiragardeshna

Hi there, Strong alignment with this project comes from delivering production-grade GenAI systems on Amazon Web Services where scalability, latency, and reliability are essential. Clear understanding of Bedrock-based architectures, orchestration using LangChain and LlamaIndex, and building serverless pipelines with Lambda, Step Functions, Glue, and Athena. Hands-on expertise with Python, vector databases, and API hardening ensures efficient retrieval, secure access, and production-ready performance. Risk is minimized through fault-tolerant ETL design, monitoring, and continuous optimization of model pipelines. Available to start immediately happy to discuss architecture and next steps. Recent work: https://www.freelancer.com/u/chiragardeshna Regards Chirag

@ffulb

Moving your GenAI proof-of-concept to production means hardening those Bedrock integrations and building bulletproof ETL pipelines. I'd start by auditing your current LangChain/LlamaIndex setup, then focus on the vector database optimization and API Gateway auth layer - that's usually where things break at scale. Built something similar with my multi-site content automation platform that processes articles autonomously using Python and AI APIs, plus a price aggregation engine tracking 800+ products with automated data pipelines. Both handle real-time processing and fault tolerance. You can see the work at ffulb.com. Available to start immediately on IST timezone. Once I can review your current AWS setup and Bedrock configuration, should be straightforward to map out the production hardening roadmap and get the first improvements deployed within days.

@Mayanks010

Hi there, You’re absolutely in the RIGHT PLACE. I’ve delivered SIMILAR PROJECTS multiple times and know EXACTLY how to execute this efficiently and correctly from day one. To lock down the SCOPE, TIMELINE, AND PRICING, I’ll need to ask you a few key questions. Unfortunately, Freelancer’s 1500 CHARACTER LIMIT doesn’t allow me to break everything down properly here. Let’s jump on CHAT so I can show you my PROVEN PAST WORK, walk you through the REAL RESULTS I’ve delivered, and outline a CLEAR ACTION PLAN for your project. You’ll immediately see why my approach is DIFFERENT and EFFECTIVE. If you’re serious about getting this done RIGHT, I’m ready to move forward. Looking forward to CONNECTING and WINNING TOGETHER. Cheers, Mayank Sahu

@ahmadraza001912

With over 4 years of experience in full-stack development, I believe I have the skills and expertise you need for your Production GenAI Platform on AWS project. My core competency lies in Python - from the language itself to utilizing its frameworks such as Django and FastAPI. This proficiency makes me well-versed in the technologies mentioned in your project description such as Amazon Bedrock, LangChain, LlamaIndex, and Mistral. In fact, I have successfully wired foundation models like Claude 3, Titan, and Llama 3 into live workloads before. Additionally, my background includes working with AWS data engineering tasks and adjacent roles for over half a decade. This extensive knowledge will come in handy while meeting your day-to-day demands, including extending and maintaining Bedrock integrations and embeddings workflow, ETL jobs design for Glue and Athena queries’ optimization and ensuring API layer's security with proper authentication and rate limits. Apart from this technical prowess, what sets me apart is my commitment to delivering clean code with clear documentation - a quality clearly essential given the complexity of your project. Moreover, I'm habituated to working within defined timelines with minimal system disruptions - exactly what you need for seamless onboarding and project execution.

@Fahadghouri7

With over 5 years of hands-on experience in AWS data engineering and the integration of cutting-edge AI solutions, I am confident that I can bring tremendous value to your GenAI project on AWS. I have an intimate knowledge of the bedrock stack and have successfully incorporated crucial foundation models like Claude 2, Titan, Llama 2, and Mistral into real-world applications. This means no learning curve for me, ensuring an immediate impact in your project from day one. The orchestration layer you rely on - LangChain, LlamaIndex, and Bedrock Agents - holds no mystery to me. I have not only worked with them before but also built custom integrations leveraging these tools to enhance overall system performance. Plus, your tech stack backed by Python (3.9+), API Gateway, Lambda, Step Functions, Athena, Glue fits my profile perfectly as Python is my domain expertise especially when it comes to building ETL jobs and API services. Don't just take my word for it though - review my comprehensive portfolio or even explore the prototypes of projects that unequivocally demonstrate my skills in building scalable web applications and integrating powerful AI systems. My approach- combining functionality with high-performance attributes is directly aligned with your project goals. I am ecstatic about this opportunity to join you in bringing this GenAI system to full production and making a real-world impact together. Let's start right away!

@aurvixlabs

Moving a GenAI PoC to production on AWS Bedrock requires hardening the orchestration layer and ETL pipeline for scale and reliability — a common pitfall is embedding workflows that don't handle unstructured data efficiently. In the mBART50 Translation API project, I built and deployed a scalable, multi-engine AI backend on AWS, directly relevant to your Bedrock/LangChain/LlamaIndex stack. My AWS ML Specialty certification and Python/AWS Lambda/Glue skills align perfectly. I'd approach this via two clear milestones for iterative delivery. Quick question — for your vector search tuning, are you prioritizing recall for RAG accuracy or latency for your API response SLAs?

@myrons7

Hi, I’m Myron Schoeman. I specialize in designing clean, responsive websites tailored to your brand and goals, emphasizing speed, usability, and modern visuals. With experience in complex AI-driven projects, I understand the importance of seamless integration and reliable performance in production environments. To tailor the solution perfectly: - Do you have preferred style guidelines or reference sites? - What key features must the site include? I am confident in delivering polished, user-friendly results on time, ensuring your project’s success from proof-of-concept to full production. Looking forward to collaborating with you.

@shailender18

As a Full Stack Developer with 6+ years of experience, a significant portion of my career has been dedicated to developing systems similar to the one you require. I’m well-versed in AWS data engineering and have spent considerable time working with key tools and languages that are integral to your project - including Amazon Bedrock, Claude 3, Titan, Llama 3, Mistral, LangChain, LlamaIndex, Python (specifically version 3.9+), API Gateway, Lambda, Step Functions and more. Not only my skills but also my approach towards projects make me uniquely suited for your needs. I am adept at focusing on clean architecture to ensure optimal performance and strong security. You can expect me to contribute well-tested code and clear documentation alongside developing fault-tolerant ETL jobs. To summarize: if you're looking for someone who can hit the ground running in delivering production-ready systems on AWS Bedrock while simultaneously managing all other aspects of your project efficiently - from designing ETL jobs optimized for Athena queries to hardening your API layer for external consumption - look no further than me.

@linhvo2202

I’m a Data and AI Engineer specializing in building intelligent systems that transform raw data and internal documents into actionable insights and AI-powered applications. My work focuses on LLM applications, Retrieval-Augmented Generation (RAG), and scalable data pipelines. I help companies build AI assistants, automate data workflows, and create systems that can efficiently search and analyze large volumes of information. What I can help you build • AI chatbots trained on company knowledge bases • Retrieval-Augmented Generation (RAG) systems for enterprise search • Automated data scraping and ingestion pipelines • Data processing and analytics platforms • Document AI systems for knowledge management Technical stack • Languages: Python, SQL, JavaScript • AI / LLM: RAG architectures, vector search, LangChain, LlamaIndex • Cloud: AWS (Bedrock, Lambda, S3, ECS, Athena) • Data: ETL pipelines, web scraping, data ingestion systems • Databases: PostgreSQL, Vector Databases, OpenSearch

@karthikresonite

Hello, I have strong experience building production-grade Generative AI systems on AWS, including integrations with Amazon Bedrock, vector databases, and serverless orchestration pipelines. I’m confident in extending POC-level GenAI systems into scalable, fault-tolerant production platforms. Relevant Experience Implemented Bedrock-based LLM workflows using Claude/Titan-style models with LangChain and LlamaIndex. Built serverless Python architectures using API Gateway, Lambda, and Step Functions. Designed ETL pipelines with AWS Glue optimized for Athena querying at scale. Configured vector search pipelines (embeddings, indexing, retrieval tuning) for latency and relevance. Hardened API layers with authentication, rate limiting, and monitoring. How I Can Contribute ✔ Extend Bedrock integrations and embeddings workflows ✔ Optimize Glue ETL and Athena performance ✔ Improve vector retrieval accuracy and speed ✔ Ensure secure, scalable API exposure ✔ Deliver clean, well-tested Python code with documentation Technical Stack Python 3.9+, AWS Lambda, Step Functions, Bedrock, LangChain, LlamaIndex, Vector DBs, Glue, Athena, API Gateway. Availability: Immediate | IST timezone compatible Engagement: Open to long-term (12-month) collaboration I can share relevant architecture examples and case studies upon request. Best regards, Resonite Technologies