Open to opportunities

Muhammad Iqbal

@muhammadiqbal8

Message

Senior AI/ML engineer building production-grade LLM systems, RAG, and real-time ML pipelines.

United States

Message

What I'm looking for

I seek roles where I can lead production-grade LLM and MLOps initiatives, drive AI strategy, mentor teams, and deliver scalable, measurable business impact in a collaborative, product-focused environment.

I am a Senior AI/ML engineer with 7+ years designing and shipping production-grade AI systems for US-based companies, specializing in LLM architectures, RAG systems, multi-agent AI, and real-time ML pipelines.

I have led migrations to Kubernetes and AWS EKS, built end-to-end MLOps platforms, and fine-tuned open-source LLMs using parameter-efficient techniques such as LoRA to balance performance and cost.

My work spans finance, SaaS, and enterprise domains, delivering TB-scale data processing, model observability, drift detection, and reusable ML components to drive operational excellence and measurable business outcomes.

Experience

Work history, roles, and key accomplishments

Current

Senior AI/ML Engineer

Current

Pienso

Oct 2023 - Present (2 years 5 months)

Architected production-grade LLM-powered text analytics and RAG knowledge assistants, migrated ML workloads to Kubernetes/EKS, and built an end-to-end MLOps platform to enable automated training, deployment, and monitoring for enterprise-scale models.

LLM RAG Kubernetes MLOps Vector Databases Model Monitoring

Machine Learning Engineer

DeepSig, Inc.

Jul 2020 - Sep 2023 (3 years 2 months)

Developed real-time ML models and deep learning NLP/signal processing pipelines for large-scale wireless systems, implemented CI/CD for model deployment, and optimized inference pipelines to improve latency and throughput.

Real Time ML Deployment Signal Processing NLP MLFlow Docker ETL

AI Engineer

Achievion Solutions

Mar 2018 - Jun 2020 (2 years 3 months)

Transitioned backend systems to AI-driven automation, built predictive analytics and Spark-based ETL pipelines, and standardized containerized deployment strategies to scale ML workloads for startups and scaleups.

Predictive Analytics Spark ETL Containerization CI CD Deployment