Abhinav Modi

AI Researcher

Bengaluru, Karnataka, India2 yrs 10 mos experience

AI ML PractitionerHighly Stable

Key Highlights

Expert in building and optimizing ML infrastructure.
Proven track record in deploying scalable AI/ML systems.
Strong problem-solving skills in high-impact environments.

Stackforce AI infers this person is a MLOps Engineer specializing in scalable AI/ML systems within the SaaS industry.

Contact

abhinavmodi.official@gmail.com LinkedIn

Skills

Core Skills

MlopsMachine Learning

Other Skills

AWS Batch TransformEventBridgeSNSFastAPISageMakerDockerKafkaGithub ActionsTerraformMLflowGenerative AIMCP ServersAgentic AIAmazon BedrockArtificial Intelligence (AI)

About

I thrive in high-ownership environments—solving complex problems, streamlining ML workflows, and building systems that scale reliably. At EagleView, I’ve consistently been trusted to take on challenging, high-impact work—often stepping in where speed, depth, and clarity are all essential. My strength lies in building and optimizing ML infrastructure—deploying SageMaker pipelines, integrating FastAPI backends, and delivering cost-efficient inference systems. I’ve worked hands-on with geospatial data, aerial imagery, and event-driven architectures across the ML lifecycle. GenAI is an area I’m steadily growing in. I’ve explored LLM-powered features, RAG pipelines, and LangChain-based prototypes, and I’m expanding this expertise through real-world implementation and continuous learning. Known for reliability, clarity in execution, and a strong sense of team ownership—I aim to raise the bar in every project I contribute to.

Experience

2 yrs 10 mos

Total Experience

2 yrs 10 mos

Average Tenure

2 yrs 10 mos

Current Experience

Eagleview

3 roles

MLOps Engineer II

Promoted

Apr 2025 – Present · 1 yr 2 mos · Bengaluru, Karnataka, India · Remote

Optimized system performance by identifying and resolving a critical I/O bottleneck, increasing GPU utilization from ~10% to ~95% and reducing overall processing time by 300%.
Designed and deployed a scalable service leveraging AWS Batch Transform, EventBridge, and SNS to efficiently process large volumes of aerial imagery.
Developed a FastAPI-based application to streamline and support the end-to-end machine learning model lifecycle.
Diagnosed a critical cloud-provider induced downtime, identified the root cause, and led rapid mitigation efforts to restore services and unblock dependent systems with minimal business impact.

AWS Batch TransformEventBridgeSNSFastAPIMLOpsMachine Learning

MLOps Engineer

Aug 2023 – Apr 2025 · 1 yr 8 mos · Bengaluru, Karnataka, India · Remote

Worked on geospatial data and aerial imagery feature extraction using ML models.
Designed and implemented a SageMaker inference pipeline, supporting real-time, asynchronous,
and batch inferencing, reducing costs by 30%. Developed a custom Docker image to run Triton
Inference Server, performing pre, post-processing & inferencing within the same container.
Ideated and developed a data registry, including DB design, to track data used for model training
and enable spot-checking of model predictions, incorporating data drift analysis.
Designed a SageMaker training pipeline with configurable parameters and benchmarking, automating
dataset preprocessing, training, and evaluation, enabling faster model training.
Supported the backend of an ML visualization tool via FastAPI, enabling on-the-fly inferencing on
imagery and integrating chatbot functionality powered by LLM support.
Connected data registry, training pipeline, and inference pipeline to create a feedback loop,
enabling continuous learning and incrementally improving model accuracy from 67% to 89%.
Optimized feature extraction pipelines, reducing execution time by 65% and costs by 50%. Deployed
and managed SageMaker, S3, Lambda, Step Functions, and SQS, designing an event-driven pipeline
using Kafka for seamless workflow orchestration. Developed and maintained AWS Lambda functions and internal Python packages to enhance operational efficiency.

SageMakerDockerFastAPIKafkaMLOpsMachine Learning

Software Engineer Intern

Feb 2023 – Jul 2023 · 5 mos · Bengaluru, Karnataka, India · Remote

Performed accuracy assessments and benchmarked YOLO models across datasets.
Built a Streamlit tool to visualize model predictions, and set up CVAT for efficient data annotation.