AI Solutions Engineer | 2+ Years Experience | Applied AI, Backend AI Systems, Autonomous AI agents.

An AI Solutions and Integration Engineer

An AI Solutions and Integration Engineer with 2 years of experience designing, optimizing, and deploying production-grade machine learning systems, autonomous agentic workflows, and high-performance backend architectures. I specialize in transforming complex AI/ML logic into scalable microservices utilizing asynchronous frameworks, containerized environments, and robust validation pipelines. My core technical expertise spans:

• Multi-Agent Coordination
• Goal-Driven Autonomous Agents
• Task Delegation Systems
• Advanced RAG (Self-RAG)
• Semantic Chunking

On the backend, I build asynchronous microservices using FastAPI, multi-threaded processing pipelines, and real-time streaming WebSockets. My MLOps and database foundation includes:

• Docker containerization
• CI/CD pipelines
• Experiment tracking via MLflow
• Model explainability (SHAP/LIME)
• Handling scalable vector databases like ChromaDB and Pinecone, alongside MongoDB for document layers

I also possess hands-on experience in computer vision and audio processing, including:

• Object detection (YOLO)
• Real-time audio transcription
• Multi-speaker diarization

Throughout my professional experience, I have delivered end-to-end automation systems with measurable ROI. At Gritstone Technologies, I designed and deployed production-grade AI systems, focusing heavily on goal-driven agentic assistants built to handle complex corporate workflows and unstructured data sets. I engineered and optimized real-time multimodal processing pipelines capable of concurrently ingesting and analyzing text, live audio streams, and visual data. I also led deep model evaluation, tracking, and continuous monitoring workflows using rigorous cross-validation, accuracy benchmarking, and latency optimization while automating deployment routines by building highly optimized Docker images and integrating automated CI/CD deployment pipelines via Docker Hub.

Prior to this, at iDatalytics, I formulated, trained, and fine-tuned classical machine learning models engineered for time-series forecasting and high-dimensional classification tasks. I collaborated closely with product teams to map out API endpoints, facilitating the smooth injection of predictive models into existing enterprise systems, and executed exhaustive feature engineering, data preprocessing, and iterative hyperparameter performance tuning on raw corporate datasets.

My enterprise portfolio includes several production-ready AI applications built for scalability and performance. I engineered a Multi-Agent Platform Orchestration System utilizing CrewAI and FastAPI where individual agents operate within restricted cognitive boundaries—featuring a Planner Agent for parsing objectives into sequential tasks, an Executor Agent for querying external APIs, and a Critic Agent for evaluating code output against programmatic validation constraints.

I built:

• An AI-Powered Interview Automation Agent that independently conducts initial technical interviews using a finite state machine, dynamically adjusting question difficulty based on an ongoing semantic vector evaluation of candidate responses and running asynchronous scoring logic that generates comprehensive grading reports.
• A CRM Automation & Lead Processing Agent that ingests unformatted inbound leads from web forms and cold emails, utilizing an extraction layer to structure data entities into clean JSON formats, executing semantic searches across corporate vector indexes via LlamaIndex to enrich profiles with intent scores, and routing them to CRM systems via a Celery/Redis background task queue to handle sudden traffic spikes.
• An Autonomous Outbound Lead Calling Agent utilizing the Twilio Voice API, FastAPI, and full-duplex WebSockets to maintain a sub-800ms response window with built-in interruption-handling algorithms that immediately flush outbound audio buffers the moment the human user starts speaking.
• An AI Resume Enhancer Agent leveraging BERT sequence classification models and LangChain to deliver precise ATS score evaluations and keyword optimization recommendations, utilizing Python's async capabilities to accelerate system evaluation speed by 50% and improve prediction accuracy by 25%.
• An AI-Powered Document Requirement Extractor using Gemini Pro and PyMuPDF to parse unstructured multi-page PDFs, bypassing standard token limits via a specialized semantic chunking algorithm that maps text boundaries based on structural thematic transitions to completely automate manual review processes.