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Vetted MLOps Professionals
Pre-screened and vetted.
Mid-level Data Scientist specializing in NLP, risk analytics, and MLOps
Mid-level AI/ML Engineer specializing in risk modeling, NLP, and Generative AI
Mid-level Data Scientist specializing in ML, MLOps, and applied risk modeling
Executive CIO and AI Transformation Leader specializing in cloud, cybersecurity, and enterprise automation
Mid-level Machine Learning Engineer specializing in Generative AI, NLP, and recommender systems
Mid-level Data Scientist specializing in GenAI, NLP, and cloud MLOps
Mid-Level Backend & Data Engineer specializing in cloud infrastructure and analytics
Mid-level Software Engineer specializing in AI and cloud data platforms
Senior Data Scientist specializing in ML engineering and cloud analytics
Mid-level AI/ML Engineer specializing in generative AI and cloud ML platforms
Mid-level AI Engineer specializing in LLMs, RAG, and multi-agent systems
Senior AI/ML Engineer specializing in MLOps and Generative AI (LLMs/RAG)
Senior Data Strategy & AI Product Consultant specializing in analytics platforms and privacy-safe measurement
Bharath Reddy Nallu
Mid-level Machine Learning Engineer specializing in NLP and scalable MLOps
“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.”
Sai Manikanta Kasireddy
Mid-level Machine Learning Engineer specializing in cloud-native GenAI and RAG systems
“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.”
Rui Zhao
Junior Machine Learning Engineer specializing in semantic search and retrieval systems
“Built and shipped a production RAG system (“TROJAN KNOWLEDGE”) for answering questions over technical PDFs, using a 3-stage retrieval stack (BM25 + FAISS + cross-encoder) to lift F1 from 71% to 84%. Drove major performance gains with a 3-level cache (memory/Redis/disk) cutting latency from ~200ms to ~10ms, and added Prometheus/Grafana monitoring plus LangChain-based fallback logic to handle OpenAI rate limits under load.”
Vikas Katuru
Junior Full-Stack AI Engineer specializing in GenAI and secure data systems
“Backend-leaning full-stack engineer who has built AI-powered analytics products from 0→1, including a predictive analytics dashboard and an AI orchestrator for natural-language-to-database querying. Particularly strong in making LLM systems production-safe through schema validation, self-healing retries, monitoring, and retrieval optimization, with quantified impact on cost, latency, and quality.”
Khuram Ismaeel
Senior AI/ML Engineer specializing in machine learning and cloud-native AI systems
“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.”
Phanindra Satya Tharun Gunturi
Mid-level DevOps/SRE Engineer specializing in cloud CI/CD, IaC, and Kubernetes
“Infrastructure engineer with deep production IBM Power/AIX experience (AIX 7.2/7.3, HMC, dual VIOS, PowerHA) supporting ~25–30 LPARs and handling live DLPAR tuning, HA failovers, and Power7→Power9 migrations. Also builds modern cloud delivery platforms—Azure DevOps CI/CD deploying Dockerized microservices to Kubernetes with Terraform-managed AWS infrastructure, strong controls around secrets, drift, and safe rollouts.”