Machine Learning Data Science

Location:

Plano, TX

Salary:

90000

Posted:

October 15, 2025

Contact this candidate

Resume:

Name: Neha Nadiminti Email: **************@*****.*** Phone Number: 682-***-**** LinkedIn

AI Research Engineer Machine Learning & Data Science Specialist

Building scalable AI systems with Generative AI, Predictive Modeling, and Cloud-native MLOps

Innovative AI/ML professional with a track record of delivering production-grade machine learning solutions across finance, healthcare, and enterprise platforms. Experienced in designing scalable ML pipelines, prototyping GenAI applications, and enabling end-to-end automation using modern MLOps workflows. Recognized for rapid adoption of emerging AI frameworks (LLMs, GANs, VAEs, LangChain) and integrating them into business-facing applications with measurable impact. Being an AI Research Engineer is more than a role to me—it’s a way to shape how organizations think, act, and grow. My journey into Data Science and AI was intentional, driven by a curiosity for intelligence and its potential to solve real problems. I thrive at the intersection of engineering and innovation, where ideas turn into scalable pipelines, predictive models, and intelligent systems that people can actually rely on. Each project is an opportunity to transform raw data into decisions that matter, whether it’s guiding financial strategies, improving healthcare outcomes, or empowering businesses to see patterns they couldn’t see before. I see myself as both a builder and a learner, constantly exploring new frameworks and refining workflows, always with the goal of creating systems that are not only technically sound but also meaningful in the impact they deliver. Achievements include:

Deployed ML pipelines at scale, reducing model deployment cycle times by up to 70% using MLflow, Kubernetes, and Terraform.

Pioneered GenAI prototypes with OpenAI APIs, Hugging Face, LangChain, enabling document summarization, conversational AI, and knowledge retrieval.

Optimized financial forecasting models with Prophet, TFT, and ARIMA, improving prediction accuracy by 15–20%.

Designed real-time APIs and dashboards (Fast API, Tableau, Power BI) that enhanced executive decision-making across multiple domains.

Profile Summary

Experienced in LLM & GenAI frameworks: GPT, BERT, Hugging Face Transformers, LangChain, LangGraph, CrewAI,

Vertex AI, AutoGen—applied to conversational AI, document intelligence, and recommendation systems.

Knowledgeable in generative modeling techniques: GANs, VAEs, and diffusion models for synthetic data, text, and image generation.

Proficient in Python, R, and SQL, applying advanced predictive modeling, classification, clustering, and segmentation techniques.

Skilled in AI/ML libraries: Scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, PyTorch for scalable ML development.

Expertise in time-series forecasting (ARIMA, SARIMA, Prophet, TFT) for demand planning, fraud anomaly detection, and financial risk modeling.

Hands-on with MLOps practices: MLflow, Docker, Kubernetes, Apache Airflow, Kedro, FastAPI, Terraform for automation and deployment.

Proficient in data engineering workflows: ETL pipeline building, Snowflake, SQL, NoSQL, MongoDB, with batch and streaming data support.

Strong visualization & reporting skills with Power BI, Tableau, Matplotlib, Seaborn, and Plotly for analytical storytelling.

Advocate of explainable and ethical AI: applying SHAP, LIME, PDP, AUC-ROC, BLEU, ROUGE, RAGAS metrics, adversarial prompt testing, HITL systems.

Cloud-native deployment experience across Azure (OpenAI, AKS), AWS (S3, Lambda, SageMaker), and GCP (Vertex AI, BigQuery, Dataflow).

Collaborative and innovation-driven professional, adept at ideation, MVP prototyping, and integrating AI into user-facing products.

Core Competencies