Databricks Architect – Cost Optimization & Agentic AI/ML (DBRX, Agent Bricks, Lakehouse)

@ZohaibRoy

⭐⭐⭐⭐⭐ Build and Optimize Your Lakehouse Platform with Databricks ❇️ Hi My Friend, I hope you are doing well. I've reviewed your project requirements and see you're looking for an experienced Databricks professional. You need not look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects focused on building and optimizing data platforms. I will design and create a robust Lakehouse platform tailored to your needs, ensuring high performance and efficiency. ➡️ Why Me? I can easily handle your Lakehouse platform project, as I have 5 years of experience in Databricks, specializing in data science and ML engineering. My expertise includes cost/performance optimization, data engineering, and AI solutions. Additionally, I have a strong grip on tools like Apache Spark, Delta Lake, and MLflow, ensuring a comprehensive approach to your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I look forward to discussing this with you! ➡️ Skills & Experience: ✅ Databricks ✅ Apache Spark ✅ Machine Learning ✅ Data Engineering ✅ AI Solutions ✅ Cloud Computing ✅ Python ✅ ETL Processes ✅ Unity Catalog ✅ Delta Lake ✅ MLflow ✅ DBRX Waiting for your response! Best Regards, Zohaib

@mubeenm5026

As an experienced software development company, we understand the vital role that a proficient Databricks Architect plays in end-to-end deployment. My expertise is not confined to advising; I am capable of implementing full production solutions using various technologies, including Apache Spark, Machine Learning, Data Engineering, AI, Cloud Computing, and Python – all requisites for this project. Notably, I have deep knowledge of the Databricks platform and its key components like Genie and Unity Catalog which will be instrumental in designing your requested AI/ML system. I'm well-versed with managing the entire machine learning lifecycle including data preprocessing (ETL), model provisioning, experimentation, alerting monitoring, retraining and more. Additionally, my understanding spans over essentials like data drift and model drift – factors that are invariably significant but often overlooked in ML integrations. Moreover, my proficiency extends to incorporating human-in-loop mechanisms for cognitive decision-making workflows. Lastly, I've demonstrated my ability to optimize performance & cost trade-offs using Databricks through efficient resource allocation strategies like canary deployment and vm design. I'm confident that our collaborative effort will yield an entirely native Databricks solution that utilizes its features comprehensively. Allow me to leverage my unique blend of skills for your project and I assure you won’t be disappointed!

@quan9824

Hi, I’m a Databricks Lakehouse engineer delivering production-grade data and AI platforms on Databricks. I design end-to-end architectures using Delta Lake, Unity Catalog, and MLflow covering ingestion (Spark/ETL), feature engineering, training, registry, deployment, monitoring, drift detection, alerting, and retraining loops. I implement scalable MLOps with model serving, canary releases, cost/performance tuning (cluster sizing, autoscaling, workload isolation), and full observability across pipelines. For agentic AI, I build multi-agent systems with DBRX, Agent Bricks, and Databricks Genie, including semantic/episodic/procedural memory, tracing, evaluation, and human-in-the-loop workflows. Strong focus on security, governance, and production reliability. I deliver fully working systems end-to-end, not just architecture or advisory work.

@PINESCRIPTERR

Hi, I can design and implement a Databricks-native Lakehouse platform covering data engineering, ML lifecycle, model serving, monitoring, and agentic AI workflows. My approach would use Unity Catalog for governance, Delta Lake for reliable data layers, Workflows/Jobs for orchestration, MLflow for experimentation/model registry, Feature Engineering where needed, Model Serving for endpoints, and cost/performance controls around cluster policies, job clusters, Photon, autoscaling, caching, partitioning, Z-ordering/liquid clustering, and workload separation. For ML lifecycle, I’d structure end-to-end pipelines for ingestion, feature generation, training, evaluation, registry promotion, canary/shadow deployment, serving, alerting, data drift/model drift checks, retraining triggers, and auditability. For agentic AI, I’d design token-efficient RAG/agent workflows using Databricks-native capabilities such as Genie/Agent Bricks where appropriate, with tracing, evaluation sets, human-in-the-loop review, memory layers, long-running task management, and observability. Question 1: Which cloud is your Databricks workspace on: AWS, Azure, or GCP? Question 2: Do you already have data sources and initial ML/agent use cases defined, or should the first milestone include discovery, architecture, and cost audit? Regards, Houssame

@Aaleen110

I understand you require a Databricks Architect to design and implement an end-to-end Lakehouse platform, focusing on cost optimization and agentic AI/ML, leveraging DBRX, Agent Bricks, and Genie. I successfully reduced cloud spend by 25% for a similar data platform while simultaneously improving ML inference speeds. My approach will involve designing the core Lakehouse architecture using Delta Lake and Unity Catalog, followed by building robust data pipelines with Apache Spark. I will implement MLflow for MLOps and deploy token-efficient agentic AI/LLM systems using DBRX and Agent Bricks, ensuring production-ready solutions. What is the expected daily inference volume for the agentic AI components? Ready to start as soon as you confirm scope.

@vishal1145

Your Lakehouse will burn through budget if cluster autoscaling is not tuned to your actual query patterns, and agentic AI workflows can rack up token costs fast without proper caching and prompt optimization. I have architected Databricks environments for 3 enterprise clients where we cut compute spend by 40% while improving pipeline performance. Quick questions - what is your current monthly Databricks spend and primary cost driver (compute vs. storage vs. model serving)? And are you planning multi-agent orchestration or single-agent workflows for the agentic AI layer? Here is the architectural approach: - DATABRICKS COST OPTIMIZATION: Implement photon-accelerated clusters with spot instance policies and query result caching to reduce redundant compute. - AGENTIC AI WITH DBRX: Build token-efficient agent workflows using MLflow for experiment tracking and Unity Catalog for governance across model versions. - LAKEHOUSE PIPELINE: Design Delta Lake medallion architecture with Spark streaming jobs and automated data quality checks using Genie for natural language queries. I have built production Lakehouse platforms handling 500GB daily ingestion with full MLOps pipelines and agent-based decision systems. Let's schedule a 20-minute architecture review to map your cost targets before implementation starts.

@dataspro

Nice to talk you , After reading in detail the requirements of your project and concluding that they match my areas of knowledge and skills, I would like to introduce myself. My name is Anthony Muñoz and I am the lead engineer for DS Pro IT agency. I have worked for over 10 years in Backend and software development and have successfully done multiple jobs. It will be a pleasure to work together to make your project a reality. Please feel free to contact me. I´m looking forward to working with you. I really appreciate your time and remain attentive to any request or question. Greetings

@alizain721

You need a Databricks Lakehouse that runs production ML and agentic LLMs while keeping DBRX costs under control — not just an architecture diagram but a working, cost audited pipeline. The real cost leaks come from duplicate data copies, idle cluster time, and naive model hosting choices combined with unoptimized token usage at inference. I built CrowdAxis, which consolidated 10 external sources into one schema, runs scheduled ETL into a model endpoint, and kept runtime costs predictable while serving real time scoring. Plan - Deliver an architecture doc and a targeted cost audit of your current DBRX spend and cluster patterns - Build a minimal working pipeline: ingestion to Delta Lake, feature lineage under Unity Catalog, MLflow model tracking, and a Genie Agent Bricks flow for orchestration - Implement token efficient LLM patterns: retrieval augmented generation, model selection and quantization, batching and request caching, plus monitoring and cost alerts - Hand over CI CD, runbook, and benchmarked cost/perf results Do you have current Databricks billing and the repo or notebook access I can audit? I’ll prepare a one page architecture diagram and a sample cost audit as the first deliverable.

@mykhaylob4

Databricks Architect – Cost Optimization & Agentic AI/ML (DBRX, Agent Bricks, Lakehouse) Open Bids 45 Average bid $1,103 USD Details Proposals Project Details $750.00 – 1,500.00 USD Bidding ends in 6 days, 1 hour Description: Need an experienced Databricks professional to design, build, and optimize an end-to-end Lakehouse platform covering data science, ML engineering, and agentic AI. Requirements: Expert Databricks cost/performance optimization and architectural trade-off judgment Hands-on: DBRX, Agent Bricks, Genie, Unity Catalog, Delta Lake, MLflow Token-efficient agentic AI/LLM system design Can implement full production solutions, not just advise Deliverables: [e.g., architecture doc, working pipeline, cost audit] Skills: Apache Spark, Machine Learning, Data Engineering, AI, Cloud Computing, Python, ETL

@sufyanasmat38

Hello With a diverse cross-skilled team like ours, we've collected experience and honed skills that are specifically tailored to the niche requirements of your project. We have an in-depth understanding and extensive hands-on experience with Databricks, including end-to-end deployment, monitoring, ML lifecycle management, experimentation, model provisioning, model serving, data drift and much more. Our expertise covers architectural trade-off judgement as well as cost and performance optimization on Databricks using methodologies such as canary deployment and more. Moreover, our proficiency in Machine Learning and AI is a strong advantage for this project task of implementing a token-efficient agentic AI/LLM system. We've worked on various aspects of ML such as image processing and object detection utilizing models like YOLO and SSD for real-time recognition; these correlate well with the nature of this task. In addition to those skills, we are knowledgeable on data engineering techniques such as Apache Spark and ETL.

@Web1devloper

As a seasoned data professional with over 8 years of expertise in various aspects of data analytics and science, I believe I have the proficiency and experience you need for your Databricks Architect project. My extensive knowledge includes working with the likes of Apache Spark, Machine Learning (familiarity with TensorFlow and PyTorch), ETL processes, as well as a solid grasp on AI and Cloud computing. Such expertise has allowed me to not just consult, but practically implement efficient solutions to harvest actionable insights from complex datasets. In terms of Lakehouse platform design and agentic AI/ML system, my work speaks for me. I've been involved with several production-level projects involving Databricks (including the coveted DBRX), Agent Bricks, MLflow, Data Lake building using Delta Lake methodology - representing my robustness with all the relevant resources you require. Taking an end-to-end approach throughout data processing - from efficient extraction to creating intuitive dashboards - is my strength. One of my key USPs is **cost efficiency**. And this isn't something that has developed overnight; it came through years of optimizing operations, enhancing customer understanding, and overdelivery on predictions.

@TechizeBuilder

Hi! I read "Databricks Architect – Cost Optimization & Agentic AI/ML (DBRX, Agent Bricks, Lakehouse)" carefully and I'm confident we can deliver. We bring extensive hands-on experience delivering enterprise-grade solutions across many industries—ERP, CRM, HRMS, LMS, web and mobile apps, e-commerce, healthcare, travel, real estate and logistics—with strong skills in Java, Python, PostgreSQL. Whatever your domain, we build secure, reliable software that's on time and within budget. Let's connect to discuss your goals, timeline and budget. Thanks!

@abanoubnashaat99

Hi, I'm a data engineer and ML practitioner experienced in building end-to-end Lakehouse architectures on Databricks — Delta Lake pipelines, Unity Catalog governance, MLflow model lifecycle management, and Spark-based ETL at scale. My work includes cost and performance optimization across cluster configurations and storage tiers, as well as production ML pipelines and LLM-powered agentic systems designed for token efficiency. I can deliver working infrastructure, not just architecture diagrams. Looking forward to it, thanks!

@mahads31

With a wealth of experience and a proven track record in data engineering, machine learning, cloud computing, and AI, I am well-equipped to undertake your Databricks Architect project. My specializations span the very tools and technologies you require such as Apache Spark, Python and especially Databricks-centric ones like DBRX, Agent Bricks, Genie and more. Being a language-agnostic engineer, I have had the privilege of working with clients from diverse industries including startups and growing businesses - that too as long-term technology partners. What this translates into is my exceptional ability to truly understand complex business requirements and design robust, scalable, and flexible systems that can evolve with your business needs. With every project Solves Inn has undertaken, we've brought clarity, strategy, execution right through development stages into delivery; aiming at reliably solving real-world problems and establishing lasting client relationships. My focus is no different for this DBRX-focused Lakehouse project – providing efficient, reliable,and token-efficient agentic AI/LLM system designs that optimize your costs along with facilitating high-quality data science and ML engineering – a crucial requirement for lasting success in this digital age.

@durgesh8527

As an experienced Full-Stack Developer, I have a deep understanding of how the data science and engineering side of the Databricks platform plays into larger systems. This includes everything from end-to-end deployment, monitoring, and ML lifecycle management to model experimentation, serving, and retraining. My expertise in Apache Spark, Cloud Computing, Python, ETL, and most significantly Databricks' DBRX, Agent Bricks, Genie, Unity Catalog, and Delta Lake make me a strong fit for your project's specific needs. Not only will I bring my technical skills to the table but also my ability to optimize systems for cost efficiency while maintaining top performance. I understand the importance of token-efficient agentic AI/LLM system design in this project and can guarantee to implement fully production-ready solutions rather than merely offering advises. To get the most out of Databricks' features and resources, it's crucial to propose native solutions using its native tools. This is what differentiates me; my previous experience harnessing all available Databricks' features and resources will allow us to streamline our work on this project right from API gateway through optimization & security making your reliance on third-party integrations minimal. I'm excited about the opportunity to leverage my unique skillset in helping you build an optimized end-to-end Lakehouse platform that truly reflects your needs. Let's discuss further.

@KarthikTechCon

Hi, I'm Karthik, a Solution Architect with 15+ years of experience in AI/ML, cloud, and data engineering, specializing in Databricks Lakehouse solutions. I have hands-on experience with Delta Lake, Unity Catalog, MLflow, DBRX, Genie, Agent Bricks, Apache Spark, Python, ETL, Model Registry, and Model Serving. My expertise covers end-to-end ML lifecycle management, CI/CD, canary deployments, monitoring, data/model drift detection, observability, multi-agent orchestration, and Databricks-native AI architectures. I also specialize in cost optimization, performance tuning (Photon, autoscaling, Z-Ordering, partitioning, caching), secure multi-workspace deployments, governance, RBAC, and production-ready Lakehouse architectures. I can deliver a fully Databricks-native solution with optimized pipelines, security, monitoring, and complete documentation. I'd be happy to discuss your architecture and implementation roadmap. Best regards, Karthik

@NEHABHAT92

As an experienced Databricks professional, I have a strong command over Apache Spark and Python, which are crucial for building a robust and high-performing Lakehouse platform. My understanding of AI and ML engineering, coupled with my ability to optimize cost/performance trade-offs, sets me apart in delivering full-fledged production solutions rather than just advising. Moreover, my expertise extends beyond data science and ML engineering into agentic AI/LLM system design. With hands-on experience on platforms like DBRX, Agent Bricks, Genie, Unity Catalog, Delta Lake, MLflow, I understand the unique challenges faced in these systems and how to overcome them. This technical knowledge when paired with my meticulous attention to detail make me the right candidate to design an efficient architecture document for your project. By choosing me for this project, not only will you get a strategic problem solver but also a dependable freelancer who will be by your side even after project delivery. Be it ETL tasks or cloud computing related issues that crop up in this project; my proficiency in these fields ensures you effective solutions. Let's turn your vision into reality together!

@ambientinfotech

Hi, We not only just write code—I build reliable solutions that solve real business problems. Whether it's automation, APIs, web applications, AI, or backend development, I can deliver exactly what you're looking for. Looking forward to working with you. Thanks, Deva

@alluankit01

Hi, I can design and implement a Databricks-native Lakehouse, ML lifecycle, and agentic AI platform with strong focus on cost optimization, performance, governance, observability, and production deployment. I’m comfortable with Databricks Lakehouse architecture, Apache Spark optimization, Delta tables, Unity Catalog governance, MLflow experimentation, model registry, model serving, retraining workflows, drift monitoring, alerting, cluster/VM sizing, job optimization, API integration, security/authentication, and agentic AI patterns including multi-agent orchestration, memory design, human-in-the-loop review, evaluation, and observability. Deliverables will include: * Databricks architecture document * Cost and performance audit * Optimized Lakehouse pipeline * ML lifecycle setup * Model serving/provisioning flow * Monitoring and alerting design * Drift/retraining strategy * Unity Catalog governance setup * Agentic AI architecture * Multi-agent workflow design * Evaluation and tracing plan * Production deployment guide I’ll focus on building a practical Databricks-native solution, not just giving advice, with clear trade-offs, cost control, secure governance, and a scalable foundation for ML and agentic AI workloads. Best regards Ankit