Ashok Tak
AI Engineer · Data Engineer · MLOps
Toronto, Canada
About
I build production GenAI systems — multi-agent architectures, RAG pipelines, and LLM-powered applications at enterprise scale. Eight years across data and AI engineering, the last two focused on shipping agentic workflows for clients in finance, energy, and e-commerce.
I love building products, using data analytics to make them better, and using AI to accelerate product development. I have a background in applied AI research and enjoy solving hard problems every day.
Now
Senior GenAI & ML Engineer at Lumenalta — architecting multi-agent systems with LangGraph, LangChain, and AWS Bedrock Agents; building enterprise RAG pipelines and FastAPI/Docker LLM services with full CI/CD.
Experience
- 2024 — present Lumenalta — Senior GenAI & ML Engineer Toronto, Canada
- 2023 — 2024 LearnAIDaily — Machine Learning Engineer Toronto, Canada
- 2021 — 2023 Scotiabank — Data & ML Engineer; Software Engineer Toronto, Canada
- 2018 — 2021 University of Toronto — ML Researcher, Cyber-Physical Systems Security Lab Toronto, Canada
- 2016 — 2018 Tata Steel — Project Manager, Utilities Jamshedpur, India
- 2015 Carnegie Mellon University — Summer Research Assistant Kigali, Rwanda
What I work with
- GenAI & LLMs
- LangChain, LangGraph, LlamaIndex, AWS Bedrock (Agents, Knowledge Bases, Guardrails), pgVector, RAG, prompt engineering
- APIs & deployment
- Python, FastAPI, Docker, Kubernetes, REST APIs, CI/CD with GitHub Actions
- MLOps
- MLflow, model registries, drift monitoring, Great Expectations, Terraform
- Data platform
- PySpark, Airflow, Kafka, Delta/Iceberg, Databricks, dbt
- Cloud
- AWS (Bedrock, SageMaker, S3, Glue), GCP (BigQuery, Vertex AI), Azure (ADF, Synapse)
Education
- MASc, Electrical & Computer Engineering — University of Toronto (2021)
- BTech, Electrical Engineering — IIT Roorkee (2016)
Certifications
- Databricks Certified ML Associate
- Databricks Certified Data Engineer Professional
- Deep Learning Specialization — deeplearning.ai
- Google Cloud Professional Data Engineer (in progress)
Research
Four peer-reviewed publications on smart grids, microgrids, and substation automation, written during my MASc and undergrad summer research at Carnegie Mellon. Thesis on ML-based fault detection in cyber-physical systems — read on TSpace.