Professional Journey
Over the past decade, I’ve crafted AI and ML solutions that helped generate more than $13 million in revenue while ensuring scalability and user focus.
My work balances engineering rigor with product insight to build impactful AI-driven tools.
Senior AI/ML Engineer – Product-Led AI Initiatives
New Era Technology (Schneider National) | Jan 2024 – Present | Hybrid
2023 - Present
What I Do:
Drive AI product strategy and develop ML/GenAI solutions that directly influence revenue and operational efficiency.
Key Impact:
Built ML-powered tools that improved bid-win rates by 8%, generating $13.4M+ annual revenue.
Partnered with PMs to define user needs, problem statements, and success metrics for AI features.
Wrote PRDs and translated business goals into clear ML problem definitions.
Designed user workflows for LLM-based assistants improving operational efficiency.
Prioritized AI backlogs and aligned engineering, analytics, and UX teams to deliver end-to-end AI features.
Served as the bridge between Product ←→ Engineering, ensuring AI solutions delivered measurable value.
What I Do:
Architect scalable ML systems and cloud-native infrastructure powering real-time decision-making.
Key Impact:
Designed a fault-tolerant ML alerting pipeline using K8s, Kafka & CA Workload Automation → Reduced costs from $1.1M → $220K/month.
Migrated legacy ML platforms into Azure ML and Kubernetes microservices → 30% cost reduction.
Built LLM-powered apps such as Cargo Document Extraction and Route Optimization Assistants.
Developed secure REST APIs with OAuth2 + Snowflake integrations for real-time analytics.
Integrated MLflow for model versioning and set standards for MLOps best practices.
Mentored junior engineers and led cloud modernization initiatives.
Senior AI/ML Engineer – MLOps, Cloud & GenAI
New Era Technology (Schneider National)
2023 - Present
Professional Experience
Senior AI/ML Engineer – Product-Led AI Initiatives
New Era Technology | Apr 2023 – Present
Key Impact: Reduced GenAI feature time-to-market by 40% and defined the strategic roadmap for enterprise AI adoption.
Strategic AI Leadership: Collaborate with product and business stakeholders to translate complex problem statements into actionable AI requirements, defining success metrics (KPIs) for accuracy, latency, and cost-efficiency.
GenAI & LLM Implementation: Design and deploy LLM-based automation tools and user workflows that directly enhance operational efficiency, acting as the technical bridge between product vision and engineering execution.
Cross-Functional Alignment: Lead initiatives across Engineering, UX, and Analytics teams to ensure AI products align with business goals, utilizing standardized PRD frameworks to streamline delivery.
Operational Reliability: Established rigorous reliability standards for AI systems, balancing scalability with cost savings in a hybrid cloud environment.
Senior AI/ML Engineer – MLOps, Cloud & GenAI
Aptude (Acquired by New Era Technology) | Jan 2022 – Mar 2023
Key Impact: Reduced monthly operational costs from $1.1M to $220K (80% reduction) and drove $13.4M+ in annual revenue through improved bid-win rates.
Pipeline Architecture: Architected a fault-tolerant, real-time ML alerting pipeline using Kubernetes, Kafka, and CA Workload Automation, significantly optimizing resource usage.
Cloud Migration: Spearheaded the migration of legacy ML platforms to Azure ML and Kubernetes microservices, achieving a 30% reduction in infrastructure overhead.
High-Impact Development: Built and deployed revenue-generating ML tools, including Intelligent Cargo Document Extraction and Delivery Route Optimization using open-source LLMs.
API & MLOps Governance: Designed secure, OAuth2-protected RESTful APIs with Snowflake integration for real-time insights and implemented MLflow for robust model versioning and experiment tracking.
Machine Learning Engineer – Cloud & MLOps
Schneider National | Jan 2021 – Dec 2021
Key Impact: Reduced deployment time by 40% through the implementation of standardized CI/CD pipelines.
Model Deployment: Designed and deployed predictive models using Python and Scikit-Learn, integrating them into production environments via Docker and Kubernetes.
API Engineering: Built high-performance Flask REST APIs to deliver real-time ML predictions, enabling instant pricing visibility and operational decision-making.
CI/CD Automation: Implemented comprehensive CI/CD workflows using GitHub Actions and Azure DevOps, increasing system reliability and accelerating release cycles.
Data Engineering: Optimized feature pipelines and automated data ingestion processes, ensuring high-quality data availability for modeling teams.
Software Engineer – Data & ML Systems
Schneider National | Jun 2018 – Dec 2020
Key Impact: Reduced manual reporting efforts by over 70% through automated data pipelines.
Data Pipeline Automation: Developed Python-based scripts to streamline data ingestion into Snowflake, significantly improving data accuracy and accessibility for analytics teams.
Legacy Modernization: Collaborated with system engineers to migrate legacy workloads into modern containerized environments, reducing technical debt.
Operational Efficiency: Automated pre-processing tasks and reporting workflows, freeing up valuable engineering time and reducing manual errors.
Monitoring & Reliability: Implemented monitoring solutions using Dynatrace to track system health and performance.
System Software Engineer
Hyve Solutions (Synnex Corp) | Jun 2017 – Apr 2018
Key Impact: Achieved a 30% boost in production efficiency by streamlining pre-production workflows.
Server Optimization: Led hardware troubleshooting and performance optimization for hyperscale clients (Amazon, Meta, eBay), focusing on OS tuning and firmware upgrades.
Automation: Developed Python and shell scripts to automate OS/firmware rollouts, reducing mean time to recovery (MTTR) and improving service reliability.
Infrastructure Management: Managed physical data center operations, including rack build-outs, power audits, and network cabling, ensuring high availability for critical server infrastructure.
PCB Validation Engineer
PCB Planet | Jun 2013 – Feb 2015
Design Validation: Analyzed and debugged complex multi-layer PCB designs (up to 24 layers) using UCAM software, ensuring compliance with manufacturing standards.
Quality Assurance: Validated schematic diagrams and technical functions for pre-production circuit boards, resolving critical design errors prior to fabrication.
Over the past decade, I’ve crafted AI and ML solutions that helped generate more than $13 million in revenue while ensuring scalability and user focus.
My work balances engineering rigor with product insight to build impactful AI-driven tools.
AI Developer
InnovateX
2015 - 2019
Built scalable ML models focusing on improving business workflows using Python and cloud tools.
Hi, I’m Jigar Shah
I build AI and ML solutions that solve real business challenges using Python, cloud, and Kubernetes.
My Passion
My Approach
Combining strong engineering with product thinking, I create AI-driven tools that improve workflows and drive revenue.
Contact
Let’s chat about AI, Python, or your project.