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Machine Learning Engineer with 4 years of experience designing, developing, and deploying scalable AI solutions across enterprise and research environments. Experienced in machine learning, deep learning, Generative AI, and LLM-powered applications. Built end-to-end AI systems across data engineering, model development, evaluation, and production deployment—delivering RAG solutions, multimodal AI, and agentic workflows at scale. Strong focus on MLOps, cloud-native architectures, scalability, reliability, and measurable business impact.…Machine Learning Engineer with 4 years of experience designing, developing, and deploying scalable AI solutions across enterprise and research environments. Experienced in machine learning, deep learning, Generative AI, and LLM-powered applications. Built end-to-end AI systems across data engineering, model development, evaluation, and production deployment—delivering RAG solutions, multimodal AI, and agentic workflows at scale. Strong focus on MLOps, cloud-native architectures, scalability, reliability, and measurable business impact.

SANKEETH KUMAR

AI Engineer, Data Scientist, Programmer, +2

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Machine Learning Engineer with 4 years of experience designing, developing, and deploying scalable AI solutions across enterprise and research environments. Experienced in machine learning, deep learning, Generative AI, and LLM-powered applications. Built end-to-end AI systems across data engineering, model development, evaluation, and production deployment—delivering RAG solutions, multimodal AI, and agentic workflows at scale. Strong focus on MLOps, cloud-native architectures, scalability, reliability, and measurable business impact.…Machine Learning Engineer with 4 years of experience designing, developing, and deploying scalable AI solutions across enterprise and research environments. Experienced in machine learning, deep learning, Generative AI, and LLM-powered applications. Built end-to-end AI systems across data engineering, model development, evaluation, and production deployment—delivering RAG solutions, multimodal AI, and agentic workflows at scale. Strong focus on MLOps, cloud-native architectures, scalability, reliability, and measurable business impact.

Available to hire

Machine Learning Engineer with 4 years of experience designing, developing, and deploying scalable AI solutions across enterprise and research environments. Experienced in machine learning, deep learning, Generative AI, and LLM-powered applications.

Built end-to-end AI systems across data engineering, model development, evaluation, and production deployment—delivering RAG solutions, multimodal AI, and agentic workflows at scale. Strong focus on MLOps, cloud-native architectures, scalability, reliability, and measurable business impact.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

Work Experience

AI/ML Engineer at Harvey AI USA

January 1, 2026 - Present

Architected a RAG-based knowledge platform for 5M+ documents, improving retrieval relevance by 35%. Optimized semantic retrieval using FAISS and Pinecone, reducing query latency by 25% while improving accuracy. Built high-performance FastAPI inference services supporting 2K+ requests/min with sub-120ms p95 latency, using batching, intelligent caching, and response streaming to improve throughput and reduce costs. Developed end-to-end LLM application pipelines integrating retrieval, reasoning, orchestration, and response generation for enterprise AI. Implemented model evaluation frameworks using offline validation and online A/B testing, defined KPIs for accuracy/latency/engagement and hallucination reduction. Built multi-agent AI systems with LangGraph for tool orchestration and memory/reasoning workflows. Drove production scalability, reliability, and cost-efficient architectural decisions for model selection and retrieval design.

ML/MLOps Engineer at Wells Fargo, USA

January 1, 2025 - December 31, 2025

Led enterprise MLOps operations using Docker, Kubernetes, MLflow, and CI/CD to enable canary deployments with automated rollback, reducing release cycles by 40%. Implemented observability via Prometheus and Grafana to monitor model drift and performance metrics (latency/throughput). Improved interpretability using SHAP and LIME for governance and validation. Automated continuous learning workflows triggered by drift and performance thresholds to maintain quality. Optimized infrastructure utilization with Kubernetes-based orchestration, reducing cloud spend by 25% while improving scalability. Built data quality and preprocessing frameworks supporting 50M+ records for reliable downstream ML. Established deployment lifecycle standards (version control, release management, rollback, environment consistency) and reinforced security with IAM controls, Kubernetes RBAC, and SSO-based authentication.

Junior ML Engineer at Infosys, India

January 1, 2021 - December 31, 2023

Delivered real-time fraud detection and anomaly detection using supervised and unsupervised learning, reducing fraudulent transactions by 18% in high-volume environments. Built NLP-driven document intelligence workflows for classification and text analytics, improving throughput by 35% through optimized tokenization, embeddings, and vectorization. Applied CNN-based deep learning for image classification and object detection, achieving 92% validation accuracy. Conducted EDA and feature engineering to improve precision and reduce noise. Orchestrated scalable data ingestion for structured and unstructured datasets to support training/validation/experimentation. Enabled production adoption via API-driven integration for business-critical processes. Optimized algorithms and resource usage to reduce processing costs by 25% while maintaining accuracy and reliability.