AI & MLOps Engineer
Problem Solver •
I engineer production-grade machine learning pipelines and architect scalable AI systems. Bridging research and production, I transform complex data challenges into reliable, measurable business impact.
About My Expertise
As an AI & MLOps Engineer, I turn experimental models into resilient, production-grade systems. From high-throughput data pipelines to scalable LLM architectures, I focus on shipping reliable intelligence that drives measurable real-world impact—not just notebook benchmarks.
I engineer across the full AI stack—spanning predictive modeling, Natural Language Processing (NLP), and domain-tuned LLMs. Whether orchestrating automated retraining loops or hardening low-latency inference APIs, every system is built for speed, scale, and uptime.
MLOps & Production ML
Automating model CI/CD, drift monitoring, and zero-downtime inference with Docker, Kubernetes, and AWS.
AI & RAG Applications
Engineering context-aware RAG pipelines, autonomous LLM agents, and low-latency semantic search.
Predictive Analytics
Turning raw, high-dimensional data into forecasting models and decision-ready intelligence.
Data Engineering
Building fault-tolerant ETL pipelines, tuned SQL engines, and real-time streaming workflows.
Skills & Technologies
Featured Projects
Let's Connect
I'm available for collaboration, consulting, or tackling data science challenges. Whether it's a project, an idea, or a new opportunity — I'd love to hear from you.
Open My Official PortfolioData Science & AI Solutions FAQ
What services do you provide as an AI & Data Science Engineer?
I specialize in machine learning engineering, end-to-end MLOps, and custom AI agents powered by RAG (Retrieval-Augmented Generation)—covering everything from data pipelines to scalable cloud deployment.
How do you approach a Machine Learning project?
My workflow follows a structured MLOps lifecycle: aligning on business KPIs, performing exploratory data analysis (EDA), iterative model experimentation, and deploying containerized APIs with automated monitoring.
Do you build production LLM and Generative AI systems?
Yes. I architect production-ready LLM applications using prompt engineering, domain fine-tuning, evaluation guardrails, and Vector Databases (Pinecone, ChromaDB, pgvector) for low-latency retrieval.
Which cloud platforms and MLOps tools do you use?
I deploy primarily on AWS and GCP using Docker, FastAPI, GitHub Actions CI/CD, and automated model registries to ensure reproducible builds and zero-downtime updates.
How do you handle data privacy and model reliability?
I implement strict data sanitization, role-based access controls, hallucination checks, and drift detection alerts so AI systems remain secure, auditable, and accurate in production.
Can you integrate AI models into existing web apps?
Yes. Beyond modeling, I build full-stack AI integrations—connecting Python ML backends and LLM pipelines directly into modern web interfaces with streaming responses and usage analytics.
What engagement models are you available for?
I am available for full-time AI/MLOps roles, technical consulting, and end-to-end freelance builds, adapting smoothly to agile product teams or standalone founder-led MVPs.
What deliverables do clients receive at project handoff?
Every engagement includes clean, documented source code, reproducible deployment scripts, architecture diagrams, and a handoff walkthrough so your team owns the system completely.
Where can I test interactive demos of your AI systems?
You can explore live architecture breakdowns and interactive project previews on my primary portfolio at https://avijitjana.vercel.app/, or reach out directly via email to discuss a custom use case.