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Vetted Machine Learning Engineers in the DFW Metroplex
Pre-screened and vetted in the DFW Metroplex.
Mid-level Full-Stack GenAI/ML Engineer specializing in agentic AI and RAG systems
Mid-level Machine Learning Engineer specializing in forecasting, NLP, and MLOps
Mid-level Full-Stack AI Engineer specializing in agentic LLM platforms
Mid-level Machine Learning Engineer specializing in MLOps and applied data science
Pranav Marla
Mid-level AI/ML & Full-Stack Engineer specializing in LLM agents and generative AI
“LLM/agent builder who shipped a live consumer AI-agent app (kalpa.chat) that visualizes complex reasoning as interactive graphs and abstracts multi-provider model usage via a unified wallet. Professionally has applied LangChain/LangGraph to IVR parsing and to scaling a football video-generation pipeline at DAZN, including shipping a VAR-specific retrieval/order fix via SQL after iterating with a non-technical PM.”
Snehitha Penumaka
Mid-level AI/ML Engineer specializing in predictive modeling and cloud ML pipelines
“LLM engineer/data engineer who has deployed production RAG systems for internal-document Q&A, building end-to-end ingestion, embedding, vector search, and FastAPI serving while actively reducing hallucinations and latency through rigorous retrieval tuning and caching. Also experienced in orchestrating cloud data pipelines (Airflow, AWS Glue, Azure Data Factory) and partnering with non-technical business teams to deliver AI solutions like automated document review.”
Mahalakshmi Konakanchi
Mid-level AI/ML Engineer specializing in Generative AI and MLOps
“Built and shipped a production RAG assistant using GPT-4, LangChain, and Pinecone/FAISS to search 50K+ institutional documents, with a strong focus on groundedness and hallucination reduction through retrieval optimization and re-ranking. Pairs this with a metrics-driven evaluation/monitoring approach (BLEU/ROUGE, manual sampling, logging) and workflow automation via Airflow, and has experience translating stakeholder needs into iterative AI prototypes.”
Saketh Kota
Mid-level Data Scientist / ML Engineer specializing in Generative AI, RAG, and MLOps
“Built and productionized a RAG-based LLM research assistant for biomedical and regulatory document search using Mixtral 7B on SageMaker, LangChain, and Milvus, cutting research time by ~40%. Has hands-on multi-cloud MLOps experience across AWS/Azure/GCP with Kubeflow/Airflow/Composer plus Terraform + ArgoCD, and applies rigorous evaluation/monitoring (latency, accuracy, hallucinations). Also partnered with a non-technical PM to deliver an insurance policy Q&A chatbot that reduced customer response time by 30%+.”
Senior Machine Learning Engineer specializing in GenAI, LLMs, and Python backend
Mid-level AI & Machine Learning Engineer specializing in Generative AI, NLP & MLOps
Mid-level Data Analyst and ML Engineer specializing in analytics, dashboards, and model deployment
Mid-level ML Software Engineer specializing in Generative AI and fraud detection
Mid-level Machine Learning Engineer specializing in NLP, Computer Vision, and LLMs
Junior ML/Data Engineer specializing in NLP, cloud data pipelines, and real-time sentiment analysis
Junior Machine Learning Software Engineer specializing in cloud-deployed predictive models
sai anuragh Sangoju
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
“Built and deployed a production LLM-powered university support chatbot on Azure using a RAG pipeline, focusing on reducing hallucinations, improving latency, and handling ambiguous queries via confidence checks and clarification prompts. Also has hands-on orchestration experience (Airflow/Azure Data Factory), including hardening a demand-forecasting ingestion workflow with sensors, retries, and automated alerts, and uses a metrics-driven testing/monitoring approach for reliable AI agents.”
Senior Full-Stack Engineer / Technical Lead specializing in cloud-native TypeScript and AI systems
Bharat Potluri
Senior Machine Learning Engineer specializing in LLMs, RAG, and agentic AI systems
“LLM/RAG practitioner who has taken a support-ticket triage automation system from prototype to production, building the full pipeline (fine-tuned models, FastAPI inference services, vector storage, monitoring) and delivering measurable impact (~40% reduction in triage time). Demonstrates strong operational troubleshooting of LLM/agentic workflows (observability-driven debugging, fixing agent routing/looping) and supports adoption through tailored demos and sales-aligned technical communication.”