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AI and Machine Learning engineer with 5 years of experience across Generative AI, Machine Learning, Data Engineering, and cloud platforms. Builds and deploys production-ready AI systems including LLMs, RAG pipelines, multi-agent systems, computer vision, and real-time data platforms, with a strong focus on automation and operational efficiency. Known for designing low-latency retrieval and evaluation workflows, reducing hallucinations through targeted context engineering, and deploying scalable systems with robust MLOps (model performance/draft monitoring, compliance, and drift mitigation) using Python, SQL, AWS/Azure/GCP, LangChain/LangGraph, and modern ML tooling.…AI and Machine Learning engineer with 5 years of experience across Generative AI, Machine Learning, Data Engineering, and cloud platforms. Builds and deploys production-ready AI systems including LLMs, RAG pipelines, multi-agent systems, computer vision, and real-time data platforms, with a strong focus on automation and operational efficiency. Known for designing low-latency retrieval and evaluation workflows, reducing hallucinations through targeted context engineering, and deploying scalable systems with robust MLOps (model performance/draft monitoring, compliance, and drift mitigation) using Python, SQL, AWS/Azure/GCP, LangChain/LangGraph, and modern ML tooling.

Dakshayaani Naraayna

AI Engineer, Data Engineer, Software Engineer, +1

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AI and Machine Learning engineer with 5 years of experience across Generative AI, Machine Learning, Data Engineering, and cloud platforms. Builds and deploys production-ready AI systems including LLMs, RAG pipelines, multi-agent systems, computer vision, and real-time data platforms, with a strong focus on automation and operational efficiency. Known for designing low-latency retrieval and evaluation workflows, reducing hallucinations through targeted context engineering, and deploying scalable systems with robust MLOps (model performance/draft monitoring, compliance, and drift mitigation) using Python, SQL, AWS/Azure/GCP, LangChain/LangGraph, and modern ML tooling.…AI and Machine Learning engineer with 5 years of experience across Generative AI, Machine Learning, Data Engineering, and cloud platforms. Builds and deploys production-ready AI systems including LLMs, RAG pipelines, multi-agent systems, computer vision, and real-time data platforms, with a strong focus on automation and operational efficiency. Known for designing low-latency retrieval and evaluation workflows, reducing hallucinations through targeted context engineering, and deploying scalable systems with robust MLOps (model performance/draft monitoring, compliance, and drift mitigation) using Python, SQL, AWS/Azure/GCP, LangChain/LangGraph, and modern ML tooling.

Available to hire

AI and Machine Learning engineer with 5 years of experience across Generative AI, Machine Learning, Data Engineering, and cloud platforms. Builds and deploys production-ready AI systems including LLMs, RAG pipelines, multi-agent systems, computer vision, and real-time data platforms, with a strong focus on automation and operational efficiency.

Known for designing low-latency retrieval and evaluation workflows, reducing hallucinations through targeted context engineering, and deploying scalable systems with robust MLOps (model performance/draft monitoring, compliance, and drift mitigation) using Python, SQL, AWS/Azure/GCP, LangChain/LangGraph, and modern ML tooling.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Work Experience

Applied AI Engineer at Bain & Company

October 1, 2024 - Present

Built an orchestration engine to automate a client “workflow debt” process by shifting manual handoffs to autonomous LangGraph/AutoGen-like workflows, reducing cycle time by 35%. Designed a low-latency RAG data layer for an automation platform using LlamaIndex and Pinecone, dropping model hallucinations by 42% via targeted context engineering. Deployed frontier LLMs on AWS in Bain’s AI Development Life Cycle (AIDLC) platform using LORA/QLoRA to reduce GPU footprint by 30%. Implemented production evaluation pipelines for an agentic code-generation engine using MLflow and “LLM-as-judge,” maintaining 99.4% compliance against live data drift. Engineered ETL/data pipelines for a healthcare AI adoption index platform on GCP BigQuery using Python (PySpark) and scikit-learn, improving client flow efficiency by 18%. Developed execution backends for internal developer platform powered by FastAPI, Cloud Code, and OpenAI Codex, accelerating software shipping times by 40% during agent orc

Machine Learning Engineer at Taskmonk Technology Pvt. Ltd.

March 1, 2020 - May 1, 2022

Developed and deployed end-to-end machine learning and deep learning pipelines for computer vision tasks (object detection, image classification, image segmentation) and multiliingual automatic speech recognition (ASR). Fine-tuned state-of-the-art architectures including YOLO, Faster-RCNN, Detectron2, OpenNMT, and Mozilla DeepSpeech on custom datasets using PyTorch and TensorFlow in AWS cloud environments. Designed and built low-latency microservices using FastAPI and Flask REST APIs to optimize and serve production-ready cloud ML models. Implemented comprehensive model evaluation frameworks with systematic A/B testing to drive iterative accuracy improvements. Performed systematic error analysis to identify model failure modes, reducing production error rates and collaborating with product teams to define and track measurable KPIs.

Data Engineer at Accenture

May 1, 2017 - August 1, 2018

Architected and maintained scalable ETL pipelines to ingest, clean, and transform high-volume multimodal datasets (text, image, audio) into AWS data lakes to support ML training and core product features. Automated data workflows and scheduled regular batch jobs and real-time streaming jobs to keep data consistently available for production applications. Implemented rigorous automated data validation and quality assurance checks across distributed pipelines, catching anomalies early and ensuring reliable data delivery. Partnered with data scientists, backend engineers, and product teams to define scalable data schemas, push experimental code into production, and establish long-term data governance standards.