Experience : 10+ yrs

Location : Bangalore / Hyderabad

AI & Agent SystemsAgent Design & Workflows:

Multi-agent systems, tool-using/self-learning agents, Model Context Protocol (MCP) for tool publishing, retrieval-augmented generation (RAG), Graph-RAG,

prompt engineering, model selection, and accuracy-per-cost routing.Evaluation & Guardrails: LLM evaluation harnesses (labeled sets, LLM-as-judge, RAGAS), guardrails, prompt-injection defense, audit trails, and rollback mechanisms.

Orchestration & AutomationWorkflow

Orchestration: Apache Airflow, AWS Step Functions, Amazon EventBridge for building durable, retryable, and idempotent AI pipelines.Task Management: Idempotent task design, retries, failure recovery, and backfills.

AI-Assisted Development

AI Coding Assistants: Claude, Cursor, Codex-style tools for scaffolding, refactoring, test generation, and code review

Cloud & DeploymentCloud Platforms:

AWS (Bedrock, S3, Glue, Lambda, EMR, Athena, CloudFormation, EventBridge), Kubernetes, Docker.Infrastructure as Code: Terraform (reusable modules), GitHub Actions, Jenkins, CI/CD, GitOps-style declarative deployments

Observability & LLMOps

- Monitoring & Tracing: Datadog, Grafana, Amazon CloudWatch, LangSmith for distributed tracing, prompt versioning, token/cost and latency monitoring, alerting, and regression-eval gating.

Data Platforms & Streaming

- Databases: Snowflake,



PostgreSQL/pgvector, MongoDB, Amazon Redshift, Delta Lake, Apache Iceberg, Oracle, SQL Server, Teradata.
- Event Streaming: Apache Kafka, Amazon Kinesis, Apache Flink.
- Vector Search: Pinecone, embeddings, chunking, reranking.

Programming & Model Training

- Languages & Frameworks: Python (pandas, PySpark, REST API), SQL, PL/SQL, Unix/Linux shell scripting, pytest.
- Model Training/Serving: Fine-tuning (LoRA/PEFT, SFT, DPO/RLHF), distillation, quantization (GGUF/AWQ/GPTQ), self-hosted inference (vLLM, TGI), Hugging Face Transformers, PyTorch

Governance, Risk & Compliance

- Controls & Validation: Regulatory reporting datasets, audit trails, RBAC, PII masking, data quality controls (Great Expectations, dbt tests), governed schemas/catalogs (AWS Glue Catalog, Erwin), dimensional/semantic data modeling.

Project Experience

- End-to-End AI System: Built and deployed a custom agentic GenAI system (AROS) using AWS Bedrock, Airflow, Pinecone, and Kubernetes, with CI/CD via GitHub Actions and Terraform, monitored by Datadog and Grafana, and governed by audit trails and RBAC.
- Data Platform Modernization: Consolidated multiple databases (Redshift, Oracle, Teradata, SQL Server) into a single governed source, orchestrated with Airflow and validated with Excellent Expectations

📌 Software Engineer (Bengaluru)
🏢 Mphasis
📍 Bengaluru