Senior AI Engineer with MLOps Expert

Jameer Ahamed Shaik

Email: **************@*****.***

Mobile: 972-***-****

LinkedIn: www.linkedin.com/in/jameer-ahamed-shaik-224082246

Ai Engineer

PROFESSIONAL SUMMARY

●AI/ML engineer with four years delivering production models, data pipelines, and GenAI features across regulated enterprises, improving decision quality, reliability, automation, and customer outcomes.

●Skilled in Python, SQL, AWS, Azure, and GCP, integrating MLOps practices, CI/CD, observability, and model monitoring to accelerate experiments and reduce deployment risk significantly.

●Built NLP, computer vision, and recommendation solutions with PyTorch, TensorFlow, and Scikit-learn, translating research into highly secure, scalable APIs, batch scoring, and real-time services.

●Collaborated with product, analytics, and platform teams, refining requirements, validating performance, and documenting governance controls to ensure compliant, maintainable, auditable enterprise machine learning systems.

TECHNICAL SKILLS

●Programming Languages - Python (Advanced), R, Scala, SQL, Bash.

●Machine Learning & Deep Learning - PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy, XGBoost, Computer Vision (CNNs, OpenCV, YOLO/OmniParser), Time Series (Prophet, LSTM), Explainable AI (SHAP, LIME).

●Generative AI & LLMs - Large Language Models (OpenAI, Gemini, Claude), AWS Bedrock, LangChain,LangGraph, RAG Architectures, Vector Databases (FAISS, Pinecone), Agents (MCP, AutoGen), Prompt Engineering, Hugging Face Transformers.

●Cloud Platforms (AWS & Azure) - AWS (SageMaker, Bedrock, Lambda, Glue, EMR, Kinesis), Azure (Azure ML, Databricks, AKS, Synapse), Serverless Computing.

●MLOps & DevOps - Docker, Kubernetes (EKS/AKS), Terraform (IaC), MLflow, Apache Airflow, CI/CD (GitHub Actions, Azure DevOps), Git.

●Big Data & Engineering - Apache Spark (PySpark), Kafka, Databricks, SQL, NoSQL (DynamoDB, MongoDB), Data Lakes (S3, ADLS), ETL Pipelines.

●NLP Tools & Libraries - SpaCy, NLTK, TextBlob, BERT, RoBERTa

●Visualization & Deployment - FastAPI, Flask, Streamlit, Power BI, Grafana, Prometheus.

PROFESSIONAL EXPERIENCE

Discover Financial Services

November 2025 – Present

Ai Engineer

●Designed credit-risk feature pipelines in Python and SQL on AWS, improving training data consistency and enabling faster, auditable model iteration across governance checkpoints enterprisewide.

●Engineered PyTorch classification models with MLflow tracking, delivering explainable predictions through REST APIs and reducing manual underwriting review effort for frontline analysts daily consistently.

●Optimized SageMaker training jobs with GPU utilization and data sharding, shortening experimentation cycles and supporting timely regulatory model validation submissions with reproducible artifacts quarterly.

●Automated CI/CD workflows in GitHub and Docker, packaging inference services for Kubernetes deployment and improving release reliability across development, test, and production environments significantly.

●Validated model performance with A/B testing and drift monitoring, surfacing degradation early and protecting portfolio outcomes through controlled rollback, recalibration, and alerting processes proactively.

HCA Healthcare

July 2025 – October 2025

Machine Learning Engineer

●Integrated clinical NLP pipelines with Transformers and Scikit-learn, extracting entities from notes and improving downstream analytics accuracy for care-operations stakeholders across facilities nationwide systemwide.

●Streamlined ETL pipelines in Airflow and Databricks, consolidating disparate datasets and enabling reproducible feature engineering for predictive readmission risk models at scale reliably consistently.

●Configured secure FastAPI services on Azure, exposing batch scoring endpoints and improving interoperability with hospital applications through documented API contracts and authentication controls end-to-end.

●Analyzed model fairness and bias metrics with Python, aligning thresholds with policy guidance and supporting transparent reporting for compliance and clinical leadership reviews routinely.

●Standardized MLOps runbooks with MLflow and Git, clarifying deployment steps and reducing on-call incidents during model releases across multiple teams and environments measurably operationally.

CGI

April 2022 – August 2024

Software Engineer

●Implemented retrieval augmented generation solutions with LangChain and vector databases, improving knowledge access for support agents and reducing time-to-resolution on complex tickets substantially measurably.

●Orchestrated multi-agent workflows with LangGraph and CrewAI, coordinating tool calls and improving automation reliability for enterprise document processing tasks across shared services securely consistently.

●Refactored data services with Node.js and PostgreSQL, enhancing throughput for feature stores and enabling scalable training data access for ML pipelines reliably continuously.

●Deployed containerized workloads with Docker, Terraform, and AWS, provisioning repeatable environments and accelerating onboarding for new project teams, stakeholders, and delivery timelines quickly operationally.

●Monitored production inference with logging, observability, and model monitoring, tracing failures quickly and maintaining service SLAs for client-facing AI applications continuously at-scale reliably systemwide.

Education

●Master's in Applied Statistics and Data Science - University of Texas At Arlington

●Bachelor's in Electronics and Communication Engineering - Sree Venkateswara College of Engineering

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