Open to opportunities

Amir Syed

@amirsyed

Machine Learning Engineer building scalable LLM and NLP systems that turn data into secure, production-ready intelligence.

United States

Message

What I'm looking for

I want to build scalable, secure, production-ready ML/GenAI systems—especially LLM evaluation/serving, NLP pipelines, and multi-agent workflows—where I can own end-to-end delivery, from data and APIs to CI/CD and observability.

I’m a Machine Learning Engineer with over 10 years of experience designing and deploying ML and backend solutions across fintech, healthcare, and enterprise domains. I focus on building NLP pipelines, multi-agent GenAI systems, and real-time predictive models, and I’m passionate about scalable, secure, high-performance applications that drive measurable operational efficiency.

Most recently, I’ve developed and deployed machine learning and generative AI workflows to evaluate and serve LLMs for enterprise use cases—using Python with FastAPI, Pydantic, SQLAlchemy, and integrations like llama.cpp and Hugging Face Transformers. I also architected relational and vector database solutions (PostgreSQL and VectorDBs), enforced fine-grained RBAC with SpiceDB, and delivered production-ready deployments through Docker, Kubernetes (EKS/ECS), Terraform, Helm, CI/CD (GitHub Actions + ArgoCD), and observability with Prometheus and Grafana.

Experience

Work history, roles, and key accomplishments

Machine Learning Engineer

Hexagon

Mar 2020 - May 2026 (6 years 2 months)

Developed and deployed generative AI and machine learning workflows to evaluate, fine-tune, and serve LLMs for enterprise use cases. Built scalable deployment pipelines, vector/relational storage for embeddings and metadata, and secure multi-tenant access controls using RBAC.

Python fastAPI Llama.Cpp LlamaIndex PostgreSQL VectorDB Kubernetes LLamacpp

Senior Python Developer

Metability LLC

Oct 2016 - Feb 2020 (3 years 4 months)

Built secure backend services and REST APIs for banking-related features using Python web frameworks and managed transactional data stores. Implemented real-time streaming and search/observability capabilities using Kafka, Elasticsearch/ELK, and AWS containerized infrastructure.

Python Django Flask Django REST Framework Apache Kafka PostgreSQL Elasticsearch Docker Kubernetes

Python Developer

Capital One

Jan 2015 - Sep 2016 (1 year 8 months)

Developed and maintained Python backend services with REST APIs and automated ETL pipelines for structured financial data. Managed AWS-hosted deployments and delivered CI/CD workflows with testing using pytest/unittest and Jenkins.