Agentic AI for Pharma Automation

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@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

@Abhi78331

As an experienced AI developer with over 5 years of expertise, I am confident that my skills will be invaluable in creating the agentic AI solution you need for your pharmaceutical automation project. With a thorough understanding of Python, TensorFlow/PyTorch, OPC UA, MQTT and modern time-series databases, I am well-versed in the toolchain that your project likely needs. Furthermore, my ability to design efficient architectures and leverage existing infrastructure will ensure seamless integration with your PLCs, SCADA historians, PAT probes and IoT sensors. Moreover, I excel at validating systems to meet regulatory standards, including CFR 21 Part 11 and Annex 11. Having worked on various high-impact web, mobile and IoT projects from conception to deployment, I am confident about constructing monitoring and control agents that can predict equipment failures and autonomously adjust critical parameters in real-time for refined process optimization. In addition to technical competence, my pitch also emphasizes on long-term partnerships. More than 70% of my clients return for future endeavors because they appreciate not only how I value both quality and punctuality above everything else but also how I provide transparency in pricing that ensures you get maximum value for your investment. Let's team up today! Abhishek (Delhi)

@KarthikTechCon

Hi, I’m Karthik, AI & Industrial Systems Architect with 15+ years building real-time optimization platforms across manufacturing, IoT, and regulated environments. Your requirement—agentic AI layered over PLC/SCADA without rip-and-replace—is exactly the right approach for GMP facilities. Proposed Architecture Data Layer OPC UA + MQTT connectors → Kafka → Time-series DB (TimescaleDB/InfluxDB) Secure, read-only ingestion with controlled write-back channel for actuator commands. Agentic AI Layer (Containerized) 1️⃣ Monitoring & Anomaly Agent (real-time drift detection) 2️⃣ Predictive Maintenance Agent (RUL modeling via PyTorch/TensorFlow) 3️⃣ Process Optimization Agent (reinforcement learning + constrained optimization) All deployed via Docker/Kubernetes with strict role-based access and audit logging. Compliance & Validation ✔ CFR 21 Part 11 / Annex 11 aligned audit trails ✔ Electronic record traceability ✔ Validation & model re-training documentation ✔ Change control & version tracking Deliverables • Full architecture & data-flow design • PLC/SCADA integration scripts • Operator dashboard (real-time KPIs & override controls) • API documentation • Validation & pilot execution plan Goal: ≥5% cycle-time reduction without deviation flags during pilot campaign. I focus on safe autonomy—AI that optimizes while respecting regulatory and operational boundaries. Let’s discuss your current historian and control topology to refine the roadmap. Regards, Karthik

@vishal1145

Your pilot will fail CFR 21 Part 11 audit if the AI agent's decision logic isn't traceable. I've seen pharma manufacturers spend six months retrofitting compliance after deploying "black box" reinforcement learning models that regulators rejected because they couldn't explain why a temperature set-point changed mid-batch. Before architecting the agent framework, I need clarity on two constraints. First, what's your current OPC UA polling frequency and historian retention policy - if you're sampling critical parameters every 30 seconds but only storing hourly aggregates, we'll miss the transient events that predict equipment drift. Second, does your site require air-gapped deployment or can the model training pipeline connect to external compute - this determines whether we use federated learning on-premise or a hybrid architecture with cloud-based retraining. Here's the technical approach: - PYTHON + PYTORCH: Build interpretable agents using attention mechanisms so every control decision maps back to specific sensor inputs, generating audit trails that satisfy 21 CFR Part 11 electronic signature requirements. - OPC UA + MQTT: Deploy edge connectors with message queuing to buffer PLC data during network hiccups, preventing the 3-5% data loss that corrupts predictive maintenance models. - TIMESCALEDB + GRAFANA: Implement a time-series stack that retains raw millisecond-level data for 90 days while compressing historical trends, supporting both real-time dashboards and retrospective batch investigations. - REINFORCEMENT LEARNING: Use constrained policy optimization where the agent learns within hard safety bounds - it can't adjust reactor temperature beyond validated ranges even if the reward function suggests it. - DOCKER + K8S: Containerize each agent with version-pinned dependencies and deploy via Kubernetes with rollback capabilities, so you can revert to manual control in under 60 seconds if an agent misbehaves. I've built three similar systems for biotech manufacturers where autonomous agents reduced batch variability by 12-18% while maintaining GMP compliance. I don't take on projects where the validation strategy is an afterthought - let's schedule a 20-minute call to walk through your current historian architecture and discuss how we'll structure the IQ/OQ/PQ protocol before writing any code.

@mayankinnovative

Hi, As per my understanding: You require an agent-based AI layer over existing PLC, SCADA, PAT, and IoT infrastructure to optimize core API/biologics manufacturing. The system must monitor critical parameters in real time, trigger autonomous adjustments, predict equipment failures, and dynamically optimize set-points—without disrupting validated systems. It must comply with GMP, CFR 21 Part 11, and Annex 11, and demonstrate ≥5% cycle-time reduction in a live pilot campaign. Implementation approach: I will design a modular architecture: data ingestion via OPC UA/MQTT into a time-series DB (e.g., InfluxDB/Timescale), an event-driven agent layer (Python, PyTorch/TensorFlow) containerized with Docker/K8s, and a secure command gateway for controlled actuator feedback. Agents will include: anomaly detection, predictive maintenance (LSTM/transformer-based), and reinforcement-learning-driven process optimization with human-in-the-loop safeguards. Full audit trails, electronic records, access controls, and validation protocols (IQ/OQ/PQ) will be built into the system. A web-based operator dashboard and REST API will complete deployment. A few quick questions: Which SCADA vendor is currently deployed? Are control loops open to supervisory AI overrides? Preferred on-prem vs hybrid cloud deployment?

@chiragardeshna

Hi there, I am a strong fit for this scope because I have designed industrial AI systems that layer on top of PLC and SCADA environments without disrupting validated manufacturing infrastructure. I have built real-time monitoring and predictive maintenance pipelines using Python, TensorFlow or PyTorch, OPC UA and MQTT ingestion, and time-series databases to drive closed-loop optimisation with strict audit logging. I would architect containerised AI agents for monitoring, anomaly detection, and set-point optimisation, orchestrated via Kubernetes, with secure connectors to PLCs and historians and a GMP-aligned validation framework addressing CFR 21 Part 11 and Annex 11. I reduce risk by isolating control logic behind supervised approval layers, implementing full traceability and electronic records compliance, validating models against historical batches before live activation, and defining measurable KPIs such as cycle-time reduction before pilot launch. I am ready to begin with a detailed architecture and validation plan aligned to your pilot cell requirements. Regards Chirag

@Srashtasoft

Hello, I’d be glad to help you design and implement an agent-based AI platform to optimise your API and biologics manufacturing lines. I have experience building AI-driven industrial systems using Python, PyTorch/TensorFlow, OPC UA/MQTT integrations, and scalable Docker/Kubernetes deployments with a strong focus on reliable, production-ready architecture. I can support: ========== ✅ Architecture & data-flow design ✅ AI agents for monitoring, predictive maintenance, and process optimisation ✅ PLC/SCADA integration with secure control layers ✅ CFR 21 Part 11 & Annexe 11–aligned validation approach ✅ Operator dashboard and API documentation A few quick questions: ==================== Which PLC/SCADA systems are currently running in the plant? Should control actions be fully autonomous or require operator approval? Do you already use a time-series database/historian? Will deployment be on-premise or hybrid cloud? What level of audit logging and model explainability is required? Happy to discuss your pilot cell goals — I’m available to start immediately. Best regards, Srashtasoft Team

@drtaimoor

Hello, your vision for an agentic AI layer over existing pharma manufacturing infrastructure aligns closely with systems we’ve architected for industrial IoT and closed-loop process optimization. I can design a GMP-aware, containerized multi-agent platform that ingests PLC/SCADA/PAT streams via OPC UA/MQTT into a time-series backbone (e.g., InfluxDB/Timescale), then deploys three coordinated agents: (1) real-time monitoring & anomaly detection, (2) predictive maintenance on equipment signatures, and (3) adaptive process optimization using reinforcement/ Bayesian tuning within validated control limits. The stack would be Python-based (PyTorch/TensorFlow), Docker/K8s deployable, with secure audit-trailed actuation interfaces and CFR 21 Part 11 / Annex 11 validation artifacts (traceability, e-signatures, change control). I would deliver architecture/data-flow, integration connectors, trained agents, operator dashboard, and validation plan, then support pilot-cell commissioning to demonstrate ≥5 % cycle-time gain without deviation events. I have prior experience integrating industrial sensor/SCADA data with AI optimization and can collaborate with your automation and QA teams to ensure compliance and safe autonomy rollout.

@Hemantt841

I understand that you need an agent-based AI system tailored to enhance efficiency in your pharmaceutical manufacturing lines, focusing specifically on real-time monitoring, autonomous adjustments, and predictive maintenance. The goal is to optimize core

@GobindSawarn

With my experience in developing practical web and app solutions, I believe I can bring immense value to your Pharma Automation project. As a skilled Python Developer, I have successfully designed and implemented ERPs, CRMs, custom dashboards, business websites and workflow automatons in the past. This broad experience has honed my expertise in compressing and organizing data sets from various sources for increased efficiency which aligns perfectly with your project's key objectives. Moreover, I am fluent in the languages and platforms you are looking to deploy – Python, TensorFlow/PyTorch, OPC UA, MQTT–you name it. Familiarity with these languages allows me to develop systems that optimize performance and minimize waste. Your need to incorporate these tools without disrupting your existing infrastructure is no challenge either. Lastly, what truly differentiates me from others is my ability to think beyond the code; I always strive to create solutions that yield tangible business outcomes. Hence, if you are looking for a highly-skilled yet mindful developer who can bring your vision of an agile agent-based system to life effectively and efficiently, look no further. Let's make this pharma automation a reality together!

@haitham1996

Hi! I’ll layer a lean, GMP-compliant AI system on top of your existing PLC/SCADA/PAT setup — real-time monitoring, auto set-point tweaks, predictive failure alerts, all in Dockerized Python agents (TensorFlow + OPC UA/MQTT + TimescaleDB) for ≥5% cycle-time gain with zero quality risk. Pilot cell runs autonomously, logs prove results, full architecture + validation (CFR 21 Part 11) delivered in 1 week — fast, secure, no rip-and-replace. Let’s jump on a chat today to map your data sources — I’m ready to make your lines the smartest & greenest in pharma!

@WiredAI

Dear Client, We can deliver this end-to-end. At WiredAI Ventures, we build agent-based AI systems for regulated manufacturing, layering intelligence over existing PLC/SCADA/PAT setups—no rip-and-replace. We’ll design GMP-compliant architecture, deploy Docker/K8s-ready agents for real-time monitoring, predictive maintenance, and set-point optimisation, and integrate via OPC UA/MQTT. Expect measurable cycle-time gains, CFR 21 Part 11 / Annex 11 validation, and a production-ready pilot. Best regards, WiredAI Ventures

@jonhw5

Hello, thanks for posting this project. Your vision for an agent-based platform that efficiently orchestrates core API and biologics manufacturing aligns well with my expertise. I have hands-on experience implementing real-time AI solutions atop existing plant infrastructure, ensuring seamless, non-disruptive integration with PLCs, SCADA, PAT, and IoT devices—while prioritizing strict regulatory compliance and reliability. I’m well-versed in designing modular AI agents using Python and frameworks like TensorFlow/PyTorch, and deploying robust solutions via Docker and Kubernetes. I’m also familiar with the intricacies of secure industrial protocols (OPC UA, MQTT) and building scalable connectors for ingesting and acting on time-series data without impacting validated plant controls. My commitment to GMP/EU Annex 11—especially 21 CFR Part 11—extends from system design through operator interface and validation planning. Your success criteria—autonomous pilot runs with validated performance improvements—match my track record of delivering measurable, documented manufacturing optimizations in regulated life sciences. Do you have a preferred time-series database or data historian in your current stack that you’d like this system to use as its primary data backbone?

@OwlWingDesigns

With extensive experience in debugging and recovering broken live systems safely, I'm confident that I can provide a streamlined solution for your Pharma Automation project. I am Swapnil, an AI developer with robust skills in Python and Software Architecture, making me an ideal candidate for your project that requires Python, TensorFlow/PyTorch, OPC UA, MQTT, and database management. My approach to problem-solving resonates perfectly with the complexity of your project. I meticulously identify the root cause of any issue and provide safe, well-documented patches without leading to data loss or any system disruptions. In view of your need to seamlessly integrate various data sources into the Agentic AI platform, my expertise in ensuring broken integrations & third-party APIs is restored will be invaluable to you. Through my work on your project, not only will you receive custom AI agents capable of monitoring & control, predicting equipment failures, and process optimization deployed in Docker or Kubernetes as per your preference; but I will also devise a hands-on validation plan compliant with CFR 21 Part 11 and Annex 11 to facilitate productively autonomous production cycles in line with your goals. Ultimately, you won't just get a fix, you'll gain a reliable and efficient system you can trust for long-term performance.

@Cassiusred

I noticed your acceptance criteria includes autonomous operation for one production campaign with 5% cycle-time reduction and zero deviation alerts. That tells me you need someone who understands both the AI/ML side and the reality of GMP-validated manufacturing environments. I would architect this as a multi-agent system in Python: one agent per concern (real-time monitoring, predictive maintenance, process optimization) communicating over MQTT, with OPC UA connectors pulling from your existing PLC/SCADA infrastructure. Each agent containerized in Docker for Kubernetes deployment so you can scale per production line. Time-series data goes into InfluxDB or TimescaleDB, with PyTorch models for the predictive maintenance and Bayesian optimization for set-point tuning. For the CFR 21 Part 11 and Annex 11 compliance piece, I would build in full audit trails, electronic signatures, and tamper-evident logging from day one rather than bolting it on later. The operator dashboard would run on FastAPI + React with real-time WebSocket feeds. I have built agentic AI systems with Python, deployed in Docker/Kubernetes, and have experience with industrial data pipelines. Happy to walk through the architecture in detail and discuss how to scope the pilot cell deployment.

@karthikresonite

Hello, We design industrial AI systems for real-time control, predictive maintenance, and GMP-compliant environments. Your requirement—agent-based optimisation layered onto existing PLC/SCADA—is exactly the right approach. ? Proposed Architecture Data Layer OPC UA + MQTT connectors Secure ingestion from PLCs, SCADA historians, PAT probes Time-series DB (TimescaleDB / InfluxDB) Immutable audit logs (21 CFR Part 11 aligned) Agent Framework (Containerised) Monitoring Agent (real-time anomaly detection) Predictive Maintenance Agent (RUL models, vibration/thermal analytics) Process Optimisation Agent (reinforcement learning / MPC hybrid) Supervisory Safety Guard (rule-based override for GMP compliance) All deployed via Docker/Kubernetes with strict role-based access and electronic signature logging. ? AI Stack Python + PyTorch/TensorFlow Feature pipelines for multivariate process control Drift detection + retraining workflow Deterministic control envelope to prevent unsafe actuation ? Deliverables ✔ Full architecture & data-flow design ✔ Containerised AI agents ✔ PLC/SCADA integration scripts ✔ CFR 21 Part 11 / Annex 11 validation plan ✔ Operator dashboard + API docs ? Success Target Pilot cell autonomous run with ≥5% cycle-time reduction and zero deviation alerts. ⏳ Estimated Timeline 16–20 weeks (design → pilot → validation) We combine AI, industrial systems, and compliance discipline—not experimental ML. Resonite Technologies