Hammad S.Private profile: Upwork shows its earnings only to signed-in members, so we check it every few weeks, not daily. MRR here is a rough estimate (Upwork's six-month earnings ÷ months active). Last read Nov 5, 2026.

Most teams hire a data engineer to build the pipelines, then a separate AI engineer to build on top of them, and lose weeks in the handoff. I do both. I'm Hammad Saleem, with 9+ years architecting cloud data infrastructure and 5+ years building the production AI systems that run on it. The data foundation (Snowflake, Databricks, Redshift, BigQuery, Airflow, multi-cloud ETL/ELT) and the AI layer (RAG, multi-agent orchestration, fine-tuned LLMs, vector search) come from one owner, architecture through deployment. That overlap of data engineering, cloud/MLOps, and LLM/agent specialization in one person is exactly what most profiles only cover half of. A few systems I've shipped recently: - A multi-agent inventory-intelligence platform for 9 automotive clients (including JLR, Penske, Holman): 5 specialized agents handling self-correcting SQL generation against Redshift, Looker integration, and reporting across 128K to 10M token contexts. - A pharma marketing AI serving 11 brands: fine-tuned request routing, multi-modal asset generation, and per-client vector stores for brand-compliant, citation-backed retrieval with HCP/DTC segmentation. - An enterprise multimodal AI platform on Llama 3.1 8B with custom LoRA fine-tuning (85.6% accuracy) and a RAG system over 480+ documents returning 3 to 4x more relevant results than baseline search. The data foundation under that work runs just as deep: pipelines processing ~3B rows/day at a national telecom, a 45% AWS cost reduction through rightsizing and storage tiering, an 80% cut in pipeline processing time, and multi-cloud financial reconciliation across Salesforce, SAP, and ad platforms that removed 70% of manual effort. Where I fit best: - Teams with messy or scattered data that needs to become a reliable foundation for analytics and AI. - Companies past the prototype stage that need an AI system to actually hold up in production: observability, retries, cost control, evaluation. - Founders and CTOs who want one engineer accountable from data ingestion through the deployed AI feature, including the full-stack/API layer that exposes it to users. How I work: I scope to outcomes, design for cost and scale before writing code, instrument everything (Opik, CloudWatch), and leave you with systems your team can run without me. I'm equally comfortable owning architecture solo or embedding with an existing team. Core stack: Python, SQL · AWS (Lambda, ECS/EKS, Redshift, S3, Step Functions) · GCP (BigQuery, Pub/Sub) · Snowflake · Databricks · Airflow, Prefect · OpenAI, Anthropic Claude, Gemini, Llama · LangChain, LlamaIndex, multi-agent orchestration, RAG, vector databases · Docker, Terraform, CI/CD. Certifications: Microsoft Azure AI Fundamentals · Microsoft Azure Data Fundamentals · IBM Machine Learning with Python · IBM Data Analysis with Python. If you're building data infrastructure, an AI product, or both, and you'd rather have one senior owner than stitch together two, send me a note with what you're trying to ship. I'll tell you honestly how I'd approach it and whether I'm the right fit. Keywords: AI engineer · AI agent developer · agentic workflows · multi-agent systems · voice AI developer · Vapi · Retell AI · conversational AI · AI chatbot development · LLM engineer · RAG developer · retrieval augmented generation · vector database · text-to-SQL · LLM fine-tuning · LoRA · self-hosted LLM · multimodal AI · OpenAI API · Anthropic Claude API · Gemini API · Amazon Bedrock · LangChain · LlamaIndex · prompt engineering · AI automation · Python · FastAPI · Next.js · Supabase · PostgreSQL · AWS Lambda · Redshift · Snowflake · BigQuery · Databricks · Airflow · dbt · ETL · ELT · data pipelines · data warehouse · data engineer · analytics engineer · Sigma · Looker · Power BI · API integration · SaaS backend · compliance automation · document processing · Airtable automation