SHOAIB MALIK MOHAMMAD
Cape Girardeau,MO
Summary
Accomplished AI/ML Engineer with 5+ years of progressive experience designing, developing, and deploying enterprise-grade AI and machine learning solutions across cloud and hybrid environments. Proven expertise in Agentic AI systems, leveraging frameworks like Crew.AI to orchestrate multi-agent workflows that enhance reasoning, adaptability, and automation in production pipelines. Experienced in designing and deploying scalable AI workloads on MCP servers, optimizing compute efficiency, model inference speed, and distributed processing performance. Extensive hands-on experience with Generative AI and Large Language Models (LLMs), including fine-tuning, prompt optimization, and Retrieval-Augmented Generation (RAG) architectures for domain-specific use cases. Demonstrated success in architecting AI ecosystems integrating LangChain, Bedrock, and vector databases to support intelligent decision-making and conversational AI systems. Strong background in deep learning frameworks such as PyTorch and TensorFlow, building scalable neural networks for NLP, computer vision, and multimodal data applications. Expertise in Python programming and automation, implementing reusable components, APIs, and microservices to accelerate model deployment and iteration cycles. Experienced in MLOps practices, developing CI/CD pipelines for machine learning workflows, model versioning, automated testing, and containerized deployments using Docker and Kubernetes. Solid understanding of cloud AI infrastructure, with practical experience on AWS, Azure, and GCP platforms for distributed model training, data storage, and scalable inference. End-to-end AI solution delivery, from problem scoping and architecture design to production deployment and post-launch performance optimization. Adept at fine-tuning transformer-based architectures (GPT, LLaMA, Falcon, Claude) for task-specific objectives while ensuring alignment, safety, and efficiency. Developed neural pipelines using vector databases (FAISS, Pinecone, Chroma) to enhance contextual accuracy in generative and reasoning-driven AI systems. Collaborated with cross-functional teams to define AI governance, model evaluation metrics, and responsible AI frameworks, ensuring compliance and trustworthiness of deployed models. Delivered scalable multi-agent AI solutions that integrate autonomous reasoning, memory persistence, and adaptive feedback mechanisms for real-time decision-making. Recognized for exceptional problem-solving and architectural design capabilities, translating complex business needs into high-impact, production-grade AI systems. Fostered innovation, mentorship, and cross-team collaboration, ensuring alignment between technical excellence and organizational AI transformation goals.
Overview
6
6
years of professional experience
Work History
AI/ML Engineer
Vanguard
Remote
01.2023 - Current
- Implemented the design and deployment of Agentic AI systems using Crew.AI and LangChain to automate telecom network optimization, fault analysis, and predictive maintenance.
- Architected scalable LLM-driven conversational agents for customer support, integrating RAG pipelines and vector databases to improve response accuracy and contextual understanding.
- Worked with cross-functional AI team of engineers and data scientists to deliver end-to-end generative AI solutions aligned with telecom operational efficiency goals.
- Integrated and optimized AI workloads on MCP servers to enhance large-scale model training, inference throughput, and distributed performance across telecom data pipelines.
- Implemented fine-tuning workflows for domain-specific LLMs leveraging PyTorch and Hugging Face Transformers, achieving significant gains in contextual precision and latency reduction.
- Designed RAG-based knowledge retrieval frameworks integrating FAISS and ChromaDB, enabling real-time access to network configuration and troubleshooting documentation.
- Orchestrated AI agent collaboration frameworks using LangGraph and Crew.AI, automating anomaly detection, ticket triage, and intelligent escalation within telecom infrastructure systems.
- Drove MLOps standardization by implementing CI/CD pipelines with MLflow, Docker, and Kubernetes, ensuring reliable model versioning and deployment reproducibility across environments.
- Collaborated with cloud architects to integrate AI workloads across AWS Bedrock and Azure AI Studio, optimizing large model inference costs and throughput.
- Designed and implemented agentic AI architectures enabling autonomous reasoning, planning, and tool use across multiple domains (data analysis, code generation, and workflow automation).
- Developed fine-tuned multi-agent frameworks using LangChain, OpenAI GPT-4/5 APIs, and AutoGen, integrating task-specific tools (retrieval, API calls, and Python execution).
- Created a self-improving AI agent that could plan, execute, and validate multi-step tasks with human-in-the-loop reinforcement, improving accuracy.
- Deployed an agent-based orchestration system for customer support automation using LLMs + vector databases (Pinecone, FAISS), reducing response time.
- Conducted R&D on emergent agent behaviors, goal-driven reasoning, and memory-augmented LLMs, enhancing contextual persistence and adaptability.
- Collaborated with cross-functional teams to integrate agentic workflows into enterprise products, supporting retrieval-augmented generation (RAG) and function calling for dynamic data querying.
- Developed multi-agent orchestration workflows combining autonomous reasoning, memory persistence, and adaptive feedback loops for intelligent decision-making in network diagnostics.
- Partnered with data engineering teams to establish feature stores and real-time data ingestion pipelines, enhancing LLM performance and reducing retraining cycles.
- Pioneered telecom-specific GenAI use cases such as automated call log summarization, root cause prediction, and intelligent knowledge base generation.
- Championed Responsible AI practices, ensuring model interpretability, bias detection, and governance in all production-grade AI and LLM deployments.
- Implemented cloud-agnostic AI architecture enabling seamless portability of models across AWS, Azure, and GCP environments for strategic scalability and cost optimization.
AI/ML Engineer
T-Mobile
Bellevue, WA
01.2023 - 12.2023
- Developed and deployed AI-driven solutions leveraging LLMs and agentic frameworks to automate data analysis, document summarization, and business intelligence workflows.
- Designed retrieval-augmented generation (RAG) pipelines integrating FAISS and Chroma to enhance contextual accuracy for financial and operational text processing.
- Implemented fine-tuning of transformer-based models using PyTorch and Hugging Face, optimizing model performance for domain-specific generative tasks and predictive analytics.
- Built autonomous agents using Crew.AI and LangChain to handle repetitive query resolution, data categorization, and intelligent information retrieval across the fid.io platform.
- Created Python-based data processing pipelines to clean, transform, and validate structured and unstructured data for training LLMs and ML models.
- Collaborated with software teams to integrate AI model endpoints using FastAPI and REST APIs, ensuring seamless interaction with production-grade applications.
- Contributed to the implementation of CI/CD and MLOps workflows using MLflow, Docker, and GitHub Actions for automated model deployment and tracking.
- Conducted A/B experiments and continuous model evaluations to measure accuracy, latency, and response quality of LLM-based features in live environments.
- Worked with AWS Bedrock and Azure AI Studio for scalable model hosting, data storage, and real-time inference optimization.
- Implemented vector search systems using Pinecone and FAISS for context retrieval, enabling LLMs to deliver domain-aware and personalized responses.
- Collaborated closely with data scientists and backend engineers to align data pipelines, model architecture, and inference APIs with business requirements.
- Utilized LangGraph and prompt chaining techniques to enhance reasoning capabilities of deployed AI agents, improving contextual flow in multi-turn conversations.
- Documented system design, data flow diagrams, and model performance metrics for transparency, compliance, and future scalability.
- Participated in peer code reviews and sprint retrospectives, ensuring high-quality implementation of AI modules and alignment with Agile development practices.
- Supported the development of responsible AI guidelines, focusing on fairness, transparency, and bias mitigation across deployed generative and predictive models.
ML Engineer
QVC Inc
Pennsylvania, USA
09.2020 - 08.2022
- Designed and implemented machine learning models to predict customer purchasing patterns and improve personalized product recommendations, increasing overall sales conversion rates.
- Developed end-to-end data pipelines using Python, Pandas, and SQL to extract, clean, and transform large volumes of transactional and behavioral data.
- Built and deployed supervised learning models with Scikit-learn and TensorFlow to forecast inventory demand and optimize stock replenishment cycles.
- Implemented NLP-based product categorization models using spaCy and NLTK, improving catalog search relevance and user browsing experience.
- Collaborated with business analysts to identify key performance metrics and train predictive models for customer churn, pricing optimization, and sales forecasting.
- Applied feature engineering techniques to enhance dataset quality, including missing value imputation, outlier detection, and categorical encoding for model readiness.
- Deployed trained models using Flask-based APIs, enabling seamless integration of predictive services within the existing e-commerce backend system.
- Conducted hyperparameter tuning, model evaluation, and cross-validation to ensure model robustness, generalization, and improved accuracy across multiple datasets.
- Built and maintained dashboards using Power BI and Matplotlib to visualize model insights, customer segmentation, and campaign performance metrics.
- Implemented collaborative filtering and content-based recommendation algorithms to deliver personalized product recommendations across customer segments.
- Partnered with the DevOps team to automate model retraining workflows and data refresh cycles, improving operational efficiency in production environments.
Education
Masters in Computer Science Engineering -
Southeast Missouri State University
Missouri12-2020
Bachelors - Electronics and Communication Engineering
Gurunank University
Hyderabad, India05-2018
Skills
- AI & Machine Learning Frameworks: PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers
- Generative AI
- Agentic AI Frameworks: CrewAI, LangChain, LangGraph, AutoGen, Semantic Kernel
- Programming Languages: Python, SQL, Bash, JavaScript
- MLOps deployment
- Data Engineering & Pipelines: Airflow, Spark, Pandas, NumPy, Data Lake, ETL, Workflows
- Vector Databases & Storage: FAISS, Pinecone, Chroma, Weaviate, Milvus
- APIs & Integrations: REST, GraphQL, FastAPI, Flask, gRPC
- Cloud platforms
- Architectural design
- Version Control & Dev Tools: Git, GitHub Actions, VS Code, Jupyter, Terraform
- Collaboration tools
Timeline
AI/ML Engineer
Vanguard
01.2023 - Current
AI/ML Engineer
T-Mobile
01.2023 - 12.2023
ML Engineer
QVC Inc
09.2020 - 08.2022
Masters in Computer Science Engineering -
Southeast Missouri State University
Bachelors - Electronics and Communication Engineering
Gurunank University