My Journey: From Data Engineer to Senior GenAI Developer

Five years ago, I started my career building ETL pipelines on Google Cloud Platform. Today, I architect enterprise-grade GenAI systems for Fortune 500 clients. Here's how that journey unfolded.

The Foundation: Data Engineering at TCS (2021–2022)

My career began with data engineering — building cloud-native ETL architectures on GCP. This phase taught me:

• How to think about data at scale
• The importance of automation (I reduced cloud costs by 40%)
• Working with BigQuery, Dataflow, and Apache Airflow

The Pivot: Data Science at Cognizant (2022–2024)

When the GenAI wave hit, I was already positioned in Cognizant's Generative AI Lab. This is where I:

• Built my first RAG pipeline with Pinecone and LangChain
• Worked with Azure OpenAI and Gemini Pro
• Won the "Rising Star" award and cleared internal SQL hackathons

The Leap: GenAI at PwC (2024–2025)

At PwC, I went from building individual features to architecting entire systems:

• Multi-agent healthcare platforms
• Enterprise RAG for financial services
• LLM orchestration with Chain Parallelism (68% inference time reduction)

Today: Senior GenAI Developer at EPAM

Now I'm building Hybrid RAG systems on AWS Bedrock and fine-tuning LLaMA/Gemma models with PEFT/LoRA — and serving as a certified technical interviewer, which keeps me close to what the industry actually asks of AI engineers.

Advice for Aspiring AI Engineers

1. Master the fundamentals — Python, SQL, statistics
2. Build in public — share your projects and learnings
3. Go deep, not wide — specialize in one area before branching out
4. Never stop learning — the field moves fast

The best time to start is now. 🚀