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Vetted AI Engineers
Pre-screened and vetted.
Mid-level AI Engineer specializing in LLM agents and orchestration
Mid-level AI/ML Engineer specializing in NLP, computer vision, and MLOps
Mid-Level Software Engineer specializing in data engineering and machine learning for FinTech
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and agentic systems
Entry-Level Software Engineer specializing in ML and Full-Stack Development
Senior AI Engineer specializing in credit risk modeling and cloud ML platforms
Junior Machine Learning Engineer specializing in LLM training and high-performance inference
Mid-level AI Engineer specializing in LLM agents, RAG, and production automation
Junior Generative AI Engineer specializing in multi-agent systems and LLM evaluation
Mid-level AI/ML Engineer specializing in MLOps, LLMs, and real-time AI systems
Mid-level AI Engineer specializing in LLM agents, RAG, and MLOps for financial services
Mid-level AI Engineer & Data Scientist specializing in Generative AI, NLP, and Cloud ML
Saikrishna Paila
Junior AI Engineer specializing in RAG pipelines and agentic AI systems
“Built and shipped production RAG/agentic systems in high-stakes domains (biomedical and legal), including an enterprise biomedical document retrieval platform over ~10k scientific docs and a multilingual African-law assistant at the World Bank. Deep hands-on experience with LangChain/LangGraph/LlamaIndex and evaluation tooling (LLM-as-a-judge, safety/hallucination detection), with measurable gains in retrieval quality and hallucination reduction.”
Harideep Balusa
Mid-level AI/ML Engineer specializing in FinTech risk, fraud detection, and GenAI/RAG systems
“Built and productionized Azure-based LLM/RAG systems for regulatory/compliance use cases, including automating analyst research and compliance report generation across large unstructured document sets. Demonstrates strong practical depth in hallucination mitigation, hybrid retrieval tuning (BM25 + embeddings), and production MLOps (Databricks, Cognitive Search, AKS, Airflow/MLflow), plus proven ability to deliver auditable, explainable solutions with non-technical compliance teams.”
Harsh Tripathi
Mid-level Machine Learning Engineer specializing in LLMs, agentic AI, and risk/fraud modeling
“Built and productionized an agentic LLM workflow during a summer internship to transform unstructured clinical reports into analytics-ready structured data, using a LangChain multi-agent design plus an LLM-as-a-judge layer to control quality in a regulated setting. Also has experience orchestrating ML pipelines at Piramal Capital using AWS Step Functions/EventBridge/CloudWatch, with strong emphasis on observability, evaluation rigor, and measurable impact (80–90% reduction in manual data entry).”
Devin Jackson
Mid-level Gameplay AI Engineer specializing in Unreal Engine
“UE5 gameplay/system designer with an engineering background who has shipped player-facing systems including an enemy weak-point feature (with replication and performance fixes) and a modular spectator minigame framework for Killer Klowns from Outer Space: The Game. Also implemented lobby mode and disconnect team-balancing (AI backfill) for Ghostbusters: Spirits Unleashed, leveraging profiling/debug tooling and cross-discipline collaboration to get features to shipping quality.”
Ojasmitha Pedirappagari
Mid-level AI Engineer specializing in LLMs, RAG, and agentic platforms
“Built and shipped a production RAG-based assistant that lets parents ask natural-language questions about their child’s learning progress, using pgvector retrieval (child-id filtered) and Redis caching to hit ~180ms latency. Implemented real-world guardrails and compliance (Llama Guard, COPPA, retrieval thresholds, fallbacks) with 99.5% uptime, and ran human-in-the-loop eval loops that improved satisfaction from 3.8 to 4.2 while serving 60k+ monthly users and reducing costs significantly.”
Hritvik Gupta
Mid-level AI Engineer specializing in LLMs, RAG, and healthcare AI
“Built and scaled an AI-powered voice/chat patient engagement platform at Penn Medicine from early prototype into production clinical workflows, focusing on latency, edge cases, and user trust. Strong in LLM reliability engineering (structured prompts, validation/fallbacks), real-time troubleshooting with observability, and cross-functional enablement through pilots, demos, and sales/customer partnership.”