LLM & retrieval systems
Grounded assistants, document ingestion, chunking, embeddings, vector search, citations, structured outputs, and evaluation-aware RAG workflows.
Open to senior AI/ML and GenAI engineering roles
I build AI systems that survive production.
Senior AI/ML Engineer with 7+ years across LLM applications, retrieval systems, retail forecasting, MLOps, distributed data pipelines, and cloud-native deployment across GCP and AWS.
Previously certified: GCP Professional ML Engineer
GCP Professional Cloud Architect
AWS ML deployment experience
Based in
Jammu, J&K
7+years in AI/ML engineering
2professional GCP credentials earned
5production domains: LLM, retail forecasting, vision, data, MLOps
Top 11%Kaggle competition finish
01 / Expertise
From model behavior to dependable systems.
I work across the full engineering path: retrieval and model orchestration, data pipelines, APIs, infrastructure, deployment, monitoring, and optimization.
Cloud & MLOps
Repeatable training and inference systems with CI/CD, containers, autoscaling services, GPU workloads, observability, and reliability controls.
Retail forecasting & applied ML
Retail demand forecasting, time-series modelling, distributed feature pipelines, customer analytics, churn and delinquency models, and production inference.
Streaming data & lakehouse systems
Configuration-driven collectors, connection recovery, schema governance, medallion layers, explainable lineage, infrastructure-as-code, and p95 observability.
02 / Selected work
Systems shaped by real constraints.
Selected work across production AI, data movement, retrieval, and deployment. Client-sensitive implementations are described without exposing private source or data.
Sentinel — RAG Quality & Safety Operations
Designed an interactive operations workflow for comparing RAG releases, investigating retrieval and grounding failures, monitoring citation coverage and safe abstention, and enforcing measurable human-controlled release gates. All metrics and traces are synthetic and clearly separated from production claims.
RAG evaluationFailure tracingRelease gatesReactTypeScriptObservability
AWS Bedrock RAG API reference implementation
Implemented a production-oriented FastAPI service for document retrieval and grounded generation on AWS, with S3 ingestion, Bedrock Knowledge Bases, least-privilege IAM, container delivery, structured logging, citations, tests, and deployment guidance. This is independent portfolio engineering and is not presented as client work.
View repositoryFastAPIBedrockS3IAMDockerCloudWatch
Synthetic MLink-to-Databricks Lakehouse
Built a configuration-driven streaming reference pipeline with authentication and subscription acknowledgements, Begin/Active/Complete checkpoints, heartbeats, deliberate disconnect recovery, Bronze/Silver/current-state/Gold layers, source lineage, p95 metrics, Docker, and automated tests. It is a protocol simulation—not a live SpiderRock integration.
View repositoryDatabricksStreamingRecoveryMedallionLineageTesting
Synthetic Streaming Signal Platform on GCP
Designed a GCP-native reference architecture with reconnect and resubscribe handling, schema governance, hash-chained evidence, explainable signal lineage, Pub/Sub, BigQuery, Secret Manager, Terraform, monitoring, and recovery tests. All messages, data, and signals are synthetic.
View repositoryGCPPub/SubBigQueryTerraformAudit chainp95
Policy intelligence and semantic search platform
Engineered a production platform for exploring policy records through semantic retrieval, combining a Dash and Flask application with Vertex AI embeddings, ScaNN search, and Cloud SQL. Delivered the supporting API and scheduled data workflows on Cloud Run with private database connectivity, Secret Manager, Artifact Registry, and workload identity federation.
Source private by design · Architecture discussable in interviewsVertex AIScaNNCloud SQLCloud RunDash
Grounded RAG and intelligent assistant systems
Designed document-aware assistants spanning ingestion, chunking, embedding generation, retrieval, citations, response structure, and cloud deployment. Focused on grounded answers and operational reliability rather than demo-only prompting.
PythonGPT-4RAGGCP
Vector embeddings ingestion pipeline
Built a Cloud Run service that reads source records from Cloud SQL, generates embeddings with Vertex AI, and writes the enriched records back to a dedicated database table.
View repositoryCloud SQLVertex AICloud Run
GCS to BigQuery delivery workflow
Automated delivery of Python notebooks and ingestion assets across development, pre-production, and production GCP environments using service accounts and a GitHub pipeline.
View repositoryGCSBigQueryCI/CD
Python interview solutions
A growing set of interview problems documented with intuition, dry runs, complexity analysis, clean Python, and automated tests—not answer dumps.
View repositoryPythonDSATesting
03 / Experience
Seven years across the AI delivery lifecycle.
Current
Independent AI/ML Engineer
Upwork · Remote
Building GPT-powered automation, RAG platforms, assistants, GCP pipelines, GPU workloads, analytics integrations, and production Python services.
2023 — 2026
AI/ML Engineer
Open Insights · US, remote
Built customer analytics and churn systems, PySpark feature pipelines, hierarchical data layers, and TensorFlow training and inference workflows.
2022 — 2023
MLOps Engineer
Capgemini · Bengaluru
Deployed ML systems on GCP, moved architectures toward serverless delivery, and introduced carbon-emission tracking for ML workflows.
2019 — 2022
ML Engineer
Hughes Systique · Gurugram
Led GCP and AWS model deployment, developed vision, NLP, and tabular models, optimized TensorFlow for mobile edge use, and maintained internal CI/CD tooling.
2019
ML Intern
Integration Wizards · Bengaluru
Trained TensorFlow and PyTorch models, prototyped pose estimation, and helped deploy a chemical-fluid leak detection system.
04 / Credentials
Certified depth, practical range.
B.Tech in Computer Science & Engineering from Shoolini University, with an OGPA of 8.14.
Google CloudPreviously certified: Professional Machine Learning Engineer
Google CloudProfessional Cloud Architect
NVIDIAJetson AI Certification
HackerRankPython Certification
05 / Recommendation
Direct manager feedback from production ML work.
A LinkedIn recommendation from a former manager who worked with me directly at Open Insights.
Bill Karr · Chief Scientist, Open Insights
Managed Raghu directly on the Open ML 1.5 platform
Raghu was a core contributor to Open ML 1.5, our hierarchical probabilistic forecasting platform serving national retail clients at scale. Over the course of our work together he became genuinely well-rounded, comfortable moving between training and deploying ML models, writing and optimizing PySpark jobs, and managing cloud infrastructure on GCP, including BigQuery and Dataproc.
Production ML
Model training, deployment, and practical delivery
Cloud engineering
GCP, BigQuery, Dataproc, and clean repo discipline
Team trust
Collaborative, dependable, and able to take ownership
06 / Contact
Have a production AI problem worth solving?
I’m interested in senior AI/ML, GenAI, MLOps, and cloud engineering opportunities where reliability matters as much as model capability.