Shamil Sherhan
Senior AI Systems Engineer | Agentic AI | LLM Applications | Data Engineering
Kochi, India · [email] · [LinkedIn]
Kochi, India · +91-9562964321 · shamil.sherhan@gmail.com · linkedin.com/in/shamilsherhan
Open to remote and freelance opportunities
Professional Summary
AI systems engineer with 7+ years of experience spanning data integration, cloud data warehousing, and production-grade LLM applications for a Fortune 500 automotive parts distribution client. Progressed from Informatica ETL development through Snowflake and BigQuery engineering to leading a team of five engineers building multi-agent AI systems that process 500K+ queries monthly.
Reduced LLM API costs by 30–50% and improved system reliability by 40%.
Strong in backend engineering (Python, FastAPI, Docker) and data infrastructure (BigQuery, Snowflake, Informatica, PySpark). Seeking senior individual contributor, technical lead, or consulting roles with ownership of AI architecture from design through production.
Technical Skills
AI/LLM Engineering
LLM applications, AI agents, Multi-agent systems, RAG, Graph retrieval, Prompt engineering, Guardrails, Evaluations, Context management, Reranking, Memory systems, Deterministic AI pipelines, OpenAI, Anthropic Claude, Azure OpenAI, Pinecone, Weaviate, Chroma
Frameworks and Tools
LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, FastAPI, Pydantic, dbt, Airflow, Prefect, Docker, Kubernetes, Git, CI/CD
Data Engineering
Informatica PowerCenter, Informatica Cloud (IICS), BigQuery, Snowflake, Advanced SQL, Data modeling, PySpark, ETL/ELT pipelines, Query optimization, Storage optimization, Data quality validation
Backend and Systems
Python, API design, REST APIs, Distributed systems, Workflow orchestration, Observability, Fault tolerance, CLI tooling, Linux, Terraform
Methodologies
Agile, Scrum, Jira, Confluence, Code review, Technical documentation, Team leadership
Professional Experience
Tata Consultancy Services (TCS) · Kochi, India ·
Fortune 500 automotive parts distribution and supply chain company
AI Systems Engineer
- Lead a team of five engineers building and deploying multi-agent AI systems for enterprise workflow automation across automotive parts distribution, supply chain optimization, and inventory management.
- Architected and deployed 10+ multi-agent systems for structured task execution, enabling autonomous decision-making across complex, multi-step processes serving 500K+ monthly queries.
- Built hybrid memory systems combining short-term context with long-term vector and graph retrieval using Pinecone and Chroma, supporting 50+ concurrent user sessions with sub-two-second response times.
- Reduced LLM API calls by 30–50% through deterministic preprocessing pipelines that resolve unambiguous cases before model invocation, saving an estimated $15K–$25K per month.
- Improved AI output reliability by 40% through structured Pydantic schemas, guardrails, and multi-layer validation across 8+ production AI services.
- Designed reusable, modular AI architectures adopted by 4+ engineering teams, reducing onboarding and development time for new AI features by 60%.
- Built high-throughput Python services handling 1M+ daily requests with logging, tracing, and metrics; containerized them with Docker and deployed them on Kubernetes.
Google BigQuery Engineer
- Designed and optimized BigQuery architectures for supply chain analytics and parts catalog management, processing 5TB+ daily with 20–40% query-performance improvements through partitioning, clustering, and materialized views.
- Reduced data infrastructure costs by 15–30%, saving $10K–$20K monthly through storage tiering, slot reservations, query cost controls, and removal of redundant processing.
- Built 30+ dbt transformation models powering inventory analytics, demand forecasting, and real-time supply chain visibility for 200+ business users.
- Developed automated data-quality validation across 100+ pipelines, reducing manual oversight by 50% and achieving 99.5% downstream data accuracy.
- Created internal CLI tooling for pipeline monitoring, alerting, and operational reporting, reducing mean issue-resolution time from 45 minutes to under 15 minutes.
Snowflake Engineer
- Led a team of 10 engineers designing scalable Snowflake warehouse architectures for enterprise analytics across parts distribution, supply chain, and sales.
- Reduced Snowflake compute and storage costs by 25–35% through warehouse sizing, auto-suspend policies, resource monitors, and query profiling.
- Built end-to-end ELT pipelines with Snowflake, dbt, and Airflow for batch and near-real-time ingestion from 50+ ERP, CRM, and POS source systems.
- Developed advanced SQL models using star and snowflake schemas plus Snowflake-native streams, tasks, and dynamic tables for downstream BI and ML workflows.
- Reduced engineering effort by 30% through modular framework design, enabling 15+ new pipeline types without greenfield builds.
- Mentored junior engineers through code reviews, pair programming, technical knowledge-sharing, and Jira-based sprint workflows.
Informatica Developer
- Designed and developed 50+ Informatica PowerCenter and IICS workflows for enterprise integration across on-premises and cloud environments supporting parts catalog and order management systems.
- Migrated legacy ETL processes to Informatica Cloud (IICS), reducing execution time by 40% and improving scheduling reliability.
- Built reusable mappings, mapplets, and session configurations that standardized ETL development and reduced development time by 35%.
- Implemented validation and reconciliation frameworks that maintained 99%+ source-to-target accuracy for critical sales, inventory, and distribution reporting.
- Managed and optimized batch workflows handling 10M+ records daily with 99.5% SLA compliance.
Education
MSc Computer Science (Machine Intelligence)
Cochin University of Science and Technology (CUSAT)
Bachelor of Computer Applications (BCA)
Kannur University