Vetted PySpark Professionals

“Data/ML engineer in financial services (Northern Trust) who built a production RAG-based LLM system to connect structured transaction/portfolio data with unstructured market and internal documents for risk teams. Strong in end-to-end pipelines (AWS Glue/Airflow/PySpark), entity resolution, and taking models from prototype to reliable daily production with performance tuning (LoRA + TensorRT) and monitoring.”

Likitha Kukunarapu

Screened References

Mid-level Applied AI Engineer specializing in data engineering and healthcare AI

Remote, USA3y exp

“Built production LLM agents spanning document Q&A, financial insight generation, and ERP-like operational data workflows, with a strong focus on reliability, grounding, and evaluation. Stands out for translating LLM systems into measurable business outcomes, including 70%–80% support workload reduction and a fallback-rate improvement from 18% to 8% through targeted RAG iteration.”

“Built and productionized an internal GenAI chatbot that makes company policy/SOP knowledge instantly searchable, using a secure RAG architecture on AWS (Bedrock/Titan embeddings/OpenSearch Serverless, Textract/Lambda/S3 ingestion, Claude 3 Sonnet). Demonstrates strong MLOps/orchestration experience (Airflow, Step Functions with Lambda/Glue/SageMaker) and a rigorous reliability approach (RAGAS metrics, A/B testing, citation validation, monitoring), including collaboration with compliance stakeholders via review dashboards.”

“ML/AI engineer with hands-on ownership of production recommendation and GenAI systems, spanning experimentation, deployment, monitoring, and iteration. Stands out for delivering measurable outcomes—22% CTR lift, 15% conversion lift, and a 30% reduction in support tickets—while demonstrating strong judgment on latency, cost, and safety tradeoffs in real-world systems.”

Laxminarayana Yaga

Screened

Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps

Missouri, USA4y exp

“Built and deployed a production RAG pipeline at PNC Financial Services to let risk/compliance analysts query millions of internal financial documents in natural language, reducing manual search and speeding regulatory validation. Demonstrates deep practical experience with large-scale document ingestion/OCR cleanup, retrieval performance tuning (hierarchical indexing, caching), and LLM reliability controls (grounding, citations, abstention), plus cloud orchestration on Azure and AWS.”

“AI/LLM engineer with healthcare domain experience who built a production clinical support “chart bot” for Molina, including PHI-safe ingestion of 200k+ PDF policies, vector retrieval, and a fine-tuned LLaMA served via vLLM on ECS Fargate. Demonstrated measurable performance wins (HNSW + namespace partitioning; 30% inference latency reduction) and a rigorous evaluation/monitoring approach, while partnering closely with nurses and operations teams to shape workflows and guardrails.”

“Infrastructure security/DevSecOps engineer who led security and automation patterns during a large Azure migration (30+ legacy apps, wave-based execution), enforcing zero-trust controls by baking CrowdStrike/Illumio into golden images and adding CI/CD security gates. Experienced integrating GitLab runners and Terraform agents as containerized services with strong secrets management (Azure Key Vault) and disciplined image/versioning practices, and has hands-on troubleshooting of ACI runtime constraints under load.”

Siva Pothuru

Screened

Mid-level AI/ML Engineer specializing in LLMs, MLOps, and cloud-native ML

San Antonio, TX5y exp

“LLM/agent engineer at USAA who built a production GPT-4o RAG conversational assistant for financial analysts, focused on regulatory interpretation and internal documentation search. Emphasizes compliance-grade reliability with strict grounding, safe fallbacks, and full auditability via MLflow/DVC plus human-in-the-loop review; reports ~45% reduction in ticket resolution time.”