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AI/ML Engineer with 4+ years of experience across financial and industrial domains, specializing in Generative AI, multi-agent workflows (LangGraph), RAG pipelines, and LLM fine-tuning. I build low-latency, production-ready AI systems with robust evaluation and guardrails. Experienced in deploying and operating LLM solutions using Python, PyTorch, LangChain, vLLM, and Kubernetes—while partnering with compliance to meet enterprise requirements. I’m especially focused on LLMOps, vector search optimization, automated retraining pipelines, and continuous quality monitoring.…AI/ML Engineer with 4+ years of experience across financial and industrial domains, specializing in Generative AI, multi-agent workflows (LangGraph), RAG pipelines, and LLM fine-tuning. I build low-latency, production-ready AI systems with robust evaluation and guardrails. Experienced in deploying and operating LLM solutions using Python, PyTorch, LangChain, vLLM, and Kubernetes—while partnering with compliance to meet enterprise requirements. I’m especially focused on LLMOps, vector search optimization, automated retraining pipelines, and continuous quality monitoring.

Sudheer Reddy Batthina

AI Engineer, Data Scientist, DevOps Developer, +2

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AI/ML Engineer with 4+ years of experience across financial and industrial domains, specializing in Generative AI, multi-agent workflows (LangGraph), RAG pipelines, and LLM fine-tuning. I build low-latency, production-ready AI systems with robust evaluation and guardrails. Experienced in deploying and operating LLM solutions using Python, PyTorch, LangChain, vLLM, and Kubernetes—while partnering with compliance to meet enterprise requirements. I’m especially focused on LLMOps, vector search optimization, automated retraining pipelines, and continuous quality monitoring.…AI/ML Engineer with 4+ years of experience across financial and industrial domains, specializing in Generative AI, multi-agent workflows (LangGraph), RAG pipelines, and LLM fine-tuning. I build low-latency, production-ready AI systems with robust evaluation and guardrails. Experienced in deploying and operating LLM solutions using Python, PyTorch, LangChain, vLLM, and Kubernetes—while partnering with compliance to meet enterprise requirements. I’m especially focused on LLMOps, vector search optimization, automated retraining pipelines, and continuous quality monitoring.

Available to hire

AI/ML Engineer with 4+ years of experience across financial and industrial domains, specializing in Generative AI, multi-agent workflows (LangGraph), RAG pipelines, and LLM fine-tuning. I build low-latency, production-ready AI systems with robust evaluation and guardrails.

Experienced in deploying and operating LLM solutions using Python, PyTorch, LangChain, vLLM, and Kubernetes—while partnering with compliance to meet enterprise requirements. I’m especially focused on LLMOps, vector search optimization, automated retraining pipelines, and continuous quality monitoring.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Beginner

Beginner

Beginner

Language

Bashkir

Intermediate

Work Experience

AI/ML Engineer at BlackRock

June 1, 2025 - Present

Engineered a multi-tenant RAG pipeline within BlackRock’s Aladdin platform using LangChain, vLLM, and Milvus, processing 500K+ unstructured financial documents to surface contextual market insights for portfolio managers. Fine-tuned Llama 3 models using QLoRA and PyTorch on proprietary asset data, improving entity extraction accuracy by 28% and reducing inference latency by 35% on distributed GPU infrastructure. Added semantic caching via Redis and tuned HNSW vector indexes in Milvus to reduce response times for high-frequency analyst queries under peak load. Built hallucination monitoring and guardrail workflows using TruLens, DeepEval, and MLflow with continuous evaluation across grounding and relevance metrics. Orchestrated automated retraining and deployment pipelines using Kubernetes, Docker, and Airflow on AWS, collaborating with Compliance on PII masking rules.

AI/ML Engineer at BlackRock, NY

June 1, 2025 - Present

Engineered a multi-tenant RAG pipeline within BlackRock's Aladdin platform using LangChain, vLLM, and Milvus, processing 500K+ unstructured financial documents to surface contextual market insights. Fine-tuned Llama 3 models using QLoRA and PyTorch on proprietary asset data, improving entity extraction accuracy by 28% and reducing inference latency by 35% on distributed GPU infrastructure. Contributed to semantic caching using Redis and HNSW vector index tuning in Milvus to reduce response times under peak analyst query load. Built hallucination monitoring and guardrail workflows using TruLens and DeepEval, enforcing continuous evaluation across RAG metrics. Orchestrated automated retraining and deployment pipelines using Kubernetes, Docker, and Airflow on AWS, collaborating with Compliance on automated PII data-masking rules.

ML Engineer at Siemens

January 1, 2021 - December 1, 2023

Developed predictive maintenance models using TensorFlow and Scikit-learn on TB-scale sensor telemetry, improving early failure detection recall by 18% across factory machinery. Built automated ETL and feature engineering pipelines with Pandas, NumPy, and SQL, reducing manual data preparation overhead by 50%. Designed and deployed containerized REST model-serving APIs in Python, achieving sub-150ms inference latency for real-time factory floor monitoring. Integrated BERT-based text processing for service-ticket classification on Azure, shortening issue triage time by 35% and increasing automated routing accuracy to 92%. Established a production model drift monitoring framework using MLflow to track feature drift via Population Stability Index (PSI), reducing false-positive alerts by 25%. Mentored 3 junior engineers on MLOps best practices, containerization, and reproducible ML workflows.

ML Engineer at Siemens, India

January 1, 2021 - December 31, 2023

Developed predictive maintenance models using TensorFlow and Scikit-Learn on TB-scale sensor telemetry, improving early failure detection recall by 18%. Built automated ETL and feature engineering pipelines with Pandas, NumPy, and SQL, reducing manual data preparation overhead by 50%. Designed and deployed containerized RESTful model-serving APIs in Python, achieving sub-150ms inference latency for real-time factory monitoring. Integrated BERT-based text processing for service-ticket classification on Azure, shortening issue triage time by 35% and improving automated routing accuracy to 92%. Established production model drift monitoring using MLflow with PSI to reduce false-positive alerts by 25% and mentored junior engineers on MLOps best practices and reproducible workflows.