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I'm an AI/ML Engineer with 7 years of experience building and deploying scalable ML and LLM-powered systems using Python, TensorFlow, PyTorch, scikit-learn, LangChain, and LangGraph. I have hands-on experience managing end-to-end model lifecycles across AWS, Azure, Databricks, and MLflow, including data pipelines, optimization, and production monitoring. I specialize in RAG systems, prompt engineering, and fine-tuning LLMs, with a strong focus on integrating AI into cloud-native, microservices architectures to drive automation and measurable business impact. I'm passionate about improving model performance and reliability at scale, implementing responsible AI practices, and delivering AI-powered analytics that empower product and business teams. I enjoy collaborating with cross-functional teams to translate complex requirements into production-grade AI solutions, from data engineering to observability and governance.…I'm an AI/ML Engineer with 7 years of experience building and deploying scalable ML and LLM-powered systems using Python, TensorFlow, PyTorch, scikit-learn, LangChain, and LangGraph. I have hands-on experience managing end-to-end model lifecycles across AWS, Azure, Databricks, and MLflow, including data pipelines, optimization, and production monitoring. I specialize in RAG systems, prompt engineering, and fine-tuning LLMs, with a strong focus on integrating AI into cloud-native, microservices architectures to drive automation and measurable business impact. I'm passionate about improving model performance and reliability at scale, implementing responsible AI practices, and delivering AI-powered analytics that empower product and business teams. I enjoy collaborating with cross-functional teams to translate complex requirements into production-grade AI solutions, from data engineering to observability and governance.

user1729966

AI Engineer, Data Scientist, Developer, +2

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I'm an AI/ML Engineer with 7 years of experience building and deploying scalable ML and LLM-powered systems using Python, TensorFlow, PyTorch, scikit-learn, LangChain, and LangGraph. I have hands-on experience managing end-to-end model lifecycles across AWS, Azure, Databricks, and MLflow, including data pipelines, optimization, and production monitoring. I specialize in RAG systems, prompt engineering, and fine-tuning LLMs, with a strong focus on integrating AI into cloud-native, microservices architectures to drive automation and measurable business impact. I'm passionate about improving model performance and reliability at scale, implementing responsible AI practices, and delivering AI-powered analytics that empower product and business teams. I enjoy collaborating with cross-functional teams to translate complex requirements into production-grade AI solutions, from data engineering to observability and governance.…I'm an AI/ML Engineer with 7 years of experience building and deploying scalable ML and LLM-powered systems using Python, TensorFlow, PyTorch, scikit-learn, LangChain, and LangGraph. I have hands-on experience managing end-to-end model lifecycles across AWS, Azure, Databricks, and MLflow, including data pipelines, optimization, and production monitoring. I specialize in RAG systems, prompt engineering, and fine-tuning LLMs, with a strong focus on integrating AI into cloud-native, microservices architectures to drive automation and measurable business impact. I'm passionate about improving model performance and reliability at scale, implementing responsible AI practices, and delivering AI-powered analytics that empower product and business teams. I enjoy collaborating with cross-functional teams to translate complex requirements into production-grade AI solutions, from data engineering to observability and governance.

Available to hire

I’m an AI/ML Engineer with 7 years of experience building and deploying scalable ML and LLM-powered systems using Python, TensorFlow, PyTorch, scikit-learn, LangChain, and LangGraph. I have hands-on experience managing end-to-end model lifecycles across AWS, Azure, Databricks, and MLflow, including data pipelines, optimization, and production monitoring. I specialize in RAG systems, prompt engineering, and fine-tuning LLMs, with a strong focus on integrating AI into cloud-native, microservices architectures to drive automation and measurable business impact.

I’m passionate about improving model performance and reliability at scale, implementing responsible AI practices, and delivering AI-powered analytics that empower product and business teams. I enjoy collaborating with cross-functional teams to translate complex requirements into production-grade AI solutions, from data engineering to observability and governance.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI/ML Engineer at Wells Fargo

July 1, 2025 - Present

Architected a cloud-native, SaaS-based agentic AI analytics platform using LLMs, RAG, LangChain, and LangGraph, leveraging OpenAI GPT-4 with Chain-of-Thought (CoT) and ReAct to enable multi-step reasoning and AI-driven data insights. Developed a distributed Supervisor Agent architecture to decompose requests and route tasks across specialized sub-agents for scalable, fault-tolerant enterprise workflows. Implemented a dual-layer semantic memory and data engineering stack with Redis for low-latency context retrieval and Pinecone for long-term semantic retention and personalization. Built and extended MCP servers to expose internal tools and knowledge sources to Claude Code and Codex agents. Integrated Anthropic Claude and OpenAI frontier models via API-driven workflows with structured prompting and tool-calling. Created Auto-RAG pipelines to validate context on demand, improving accuracy and reducing hallucinations. Built FastAPI services to expose agentic workflows and MCP integrations.

Machine Learning Engineer at HCLTech

November 1, 2020 - July 1, 2025

Built scalable AI-powered anomaly detection and analytics application using Autoencoders and CMGOS on Databricks to identify real-time outliers in 5+ years of sensor data, driving operational insights and reducing downtime. Designed PySpark-based data pipelines to ingest, cleanse, and process high-volume sensor data from PI Systems for ML, reporting, and dashboards. Implemented CI/CD and configuration management with Git and YAML to improve reproducibility and deployment efficiency. Optimized SQL analytics workloads across SSMS, Hive, and Impala with indexing and partitioning, reducing latency. Improved model accuracy by reducing false positives through feature engineering and threshold tuning. Integrated real-time anomaly alerts with Power BI dashboards and deployed cloud-native pipelines on Azure AKS for scalable, fault-tolerant operations. Implemented automated drift detection, retraining pipelines, and telemetry-based monitoring to sustain long-term performance. Collaborated with c

Software Engineer at Screative’s Software Services Private Limited

March 1, 2019 - November 1, 2020

Built a default risk classification model using XGBoost and SMOTE, improving prediction accuracy from 81% to 94% and reducing loan approval time. Migrated batch ETL processes to PySpark on Hadoop, processing 10TB+ of financial data weekly and reducing computation time by 60%. Built and optimized risk and anomaly detection models to ensure data integrity in real-time transaction environments. Deployed ML models using Docker and CI/CD pipelines on Azure Kubernetes Service, enabling bi-weekly model updates for real-time decisioning platforms. Created APIs and Power BI dashboards for real-time decision platforms, boosting customer satisfaction and partnering with data scientists to transition research models into production-ready services.