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AI Engineer with five years across AI/ML and software engineering (2021–2026), specializing in production LLM applications, agentic systems, and AI governance for regulated enterprises. I’ve delivered RAG and agent-based solutions in financial services at JPMorgan Chase and HSBC—improving compliance Q&A resolution time from 47 minutes to 8 minutes, achieving 91.3% F1 for contract risk classification, and producing explainable credit risk scoring with 0.91 AUC for regulatory stakeholders. I build evaluation and observability frameworks for LLM quality (RAGAS, TruLens, Langfuse, Arize) and design robust policy enforcement with hard guardrails, kill switches, and immutable audit trails for autonomous agents. I’m strong in Python, SQL, REST APIs, vector retrieval, and LLMOps across AWS, GCP, and Azure, with a focus on reliable, release-ready systems.…AI Engineer with five years across AI/ML and software engineering (2021–2026), specializing in production LLM applications, agentic systems, and AI governance for regulated enterprises. I’ve delivered RAG and agent-based solutions in financial services at JPMorgan Chase and HSBC—improving compliance Q&A resolution time from 47 minutes to 8 minutes, achieving 91.3% F1 for contract risk classification, and producing explainable credit risk scoring with 0.91 AUC for regulatory stakeholders. I build evaluation and observability frameworks for LLM quality (RAGAS, TruLens, Langfuse, Arize) and design robust policy enforcement with hard guardrails, kill switches, and immutable audit trails for autonomous agents. I’m strong in Python, SQL, REST APIs, vector retrieval, and LLMOps across AWS, GCP, and Azure, with a focus on reliable, release-ready systems.

Sivaji Alla

AI Engineer, Software Engineer, Data Scientist, +2

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AI Engineer with five years across AI/ML and software engineering (2021–2026), specializing in production LLM applications, agentic systems, and AI governance for regulated enterprises. I’ve delivered RAG and agent-based solutions in financial services at JPMorgan Chase and HSBC—improving compliance Q&A resolution time from 47 minutes to 8 minutes, achieving 91.3% F1 for contract risk classification, and producing explainable credit risk scoring with 0.91 AUC for regulatory stakeholders. I build evaluation and observability frameworks for LLM quality (RAGAS, TruLens, Langfuse, Arize) and design robust policy enforcement with hard guardrails, kill switches, and immutable audit trails for autonomous agents. I’m strong in Python, SQL, REST APIs, vector retrieval, and LLMOps across AWS, GCP, and Azure, with a focus on reliable, release-ready systems.…AI Engineer with five years across AI/ML and software engineering (2021–2026), specializing in production LLM applications, agentic systems, and AI governance for regulated enterprises. I’ve delivered RAG and agent-based solutions in financial services at JPMorgan Chase and HSBC—improving compliance Q&A resolution time from 47 minutes to 8 minutes, achieving 91.3% F1 for contract risk classification, and producing explainable credit risk scoring with 0.91 AUC for regulatory stakeholders. I build evaluation and observability frameworks for LLM quality (RAGAS, TruLens, Langfuse, Arize) and design robust policy enforcement with hard guardrails, kill switches, and immutable audit trails for autonomous agents. I’m strong in Python, SQL, REST APIs, vector retrieval, and LLMOps across AWS, GCP, and Azure, with a focus on reliable, release-ready systems.

Available to hire

AI Engineer with five years across AI/ML and software engineering (2021–2026), specializing in production LLM applications, agentic systems, and AI governance for regulated enterprises. I’ve delivered RAG and agent-based solutions in financial services at JPMorgan Chase and HSBC—improving compliance Q&A resolution time from 47 minutes to 8 minutes, achieving 91.3% F1 for contract risk classification, and producing explainable credit risk scoring with 0.91 AUC for regulatory stakeholders.

I build evaluation and observability frameworks for LLM quality (RAGAS, TruLens, Langfuse, Arize) and design robust policy enforcement with hard guardrails, kill switches, and immutable audit trails for autonomous agents. I’m strong in Python, SQL, REST APIs, vector retrieval, and LLMOps across AWS, GCP, and Azure, with a focus on reliable, release-ready systems.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

Senior AI/ML Engineer at Qualcomm Research Lab

November 1, 2025 - Present

Built production RAG and semantic search components using LlamaIndex/FAISS for low-latency inference and privacy constraints. Led fine-tuning of LLaMA 2-7B using QLoRA/PEFT, defined release-grade LLM evaluation and observability frameworks (RAGAS, Langfuse, Arize), and standardized model qualification processes. Architected real-time inference services using FastAPI and Triton on AWS SageMaker with autoscaled Kubernetes. Implemented LLM evaluation/traceability and MLOps lifecycle orchestration via MLflow/Kubeflow/W&B. Deployed optimized models to edge using SNPE with INT8 quantization and knowledge distillation; mentored engineers on LLM evaluation and MLOps best practices.

AI/ML Engineer at JPMorgan Chase

March 1, 2025 - July 31, 2025

Delivered a LangChain-based RAG platform over internal compliance documentation with source-grounded responses backed by citable controlling documents. Designed an enterprise contract risk classification workflow using RoBERTa fine-tuning over legal clauses, achieving 91.3% F1 and replacing a manual review process. Partnered with Legal/Compliance to translate regulatory requirements into technical specifications and delivered multiple production models under approval gates. Built data pipelines with Spark/Airflow/dbt feeding BigQuery; containerized inference microservices on GCP Vertex AI; and reduced cloud costs through autoscaling.

AI/ML Engineer at Hexagon

August 1, 2022 - August 31, 2023

Engineered high-throughput REST APIs in Java Spring Boot for supply chain order management across regional hubs. Built demand forecasting using Scikit-learn and XGBoost ensembles to reduce overstock inventory costs. Refactored batch jobs into Python + Airflow DAG pipelines to reduce nightly processing time. Translated business requirements into backend features using Java Spring Boot and PostgreSQL, improving order processing accuracy.

AI/ML Engineer Intern at HSBC

September 1, 2021 - February 28, 2022

Built an explainable credit risk prediction framework (XGBoost, LightGBM, Scikit-learn, SHAP) using 2M+ customer and transaction records, achieving 0.91 AUC and improving default prediction performance for regulatory and business stakeholders. Developed a document intelligence platform using OCR, spaCy, and transformer models to extract customer/KYC/loan application data from large document volumes, reducing manual effort and improving validation accuracy. Created dashboards and automated ML pipelines to monitor risk, segmentation, fraud trends, and portfolio performance; reduced reporting turnaround from days to under 4 hours.