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Vetted Machine Learning Engineers in Remote
Pre-screened and vetted in Remote.
Senior Software Engineer specializing in AI/ML evaluation and full-stack systems
Senior Machine Learning Engineer specializing in LLMs and Generative AI
Mid-level AI/ML Engineer specializing in fraud detection and customer lifetime value modeling
Mid-level AI/ML Engineer specializing in LLMs, RAG, and enterprise AI
Sharath Nyalakonda
Mid-level AI/ML Engineer specializing in NLP, graph models, and MLOps for FinTech and Healthcare
“AI/ML engineer who has deployed production LLM/transformer-based systems for merchant intelligence and fraud/support optimization, delivering +27% merchant engagement and +18% payment success. Deep experience in privacy-preserving, PCI DSS-compliant data/ML pipelines (Airflow, AWS Glue, Spark, Delta Lake) and scalable microservices on Kubernetes, plus proven cross-functional delivery in healthcare claims analytics at UnitedHealth Group (12% HEDIS claim reduction).”
Zahan Goel
Junior AI/ML Engineer specializing in LLM systems and mechanistic interpretability
“Second most active contributor at Daice Labs, owning a production AI-powered software development collaboration platform’s end-to-end execution infrastructure (TypeScript/Next.js backend, Node.js CLI, shared libs). Built the full multi-agent pipeline (planning/codegen/summary), Supabase-backed context assembly and realtime state, Git/GitHub automation, and a provider-agnostic LLM abstraction with strict Zod validation and retries, backed by extensive tests and design specs.”
Meghashree N
Mid-level AI/ML Engineer specializing in recommender systems, NLP, and cloud ML
“AI/ML engineer who has shipped both a safety-critical mental health RAG chatbot (Mistral 7B + Pinecone) with automated faithfulness/toxicity monitoring and a deep Q-learning investment recommendation engine at Lincoln Financial Group. Strong in production MLOps and orchestration (AWS Lambda/CloudWatch/SageMaker, Docker, AKS) and in translating regulated-domain requirements (clinical reliability, fiduciary duty) into measurable model constraints and monitoring.”
Ashutosh Choudhari
Mid-level AI/ML Engineer specializing in LLM applications and cloud-native systems
“LLM engineer who has shipped production AI systems, including an RFP requirements extraction platform (OpenAI o4-mini + Azure AI Search + FastAPI) achieving 90%+ accuracy and ~5x throughput through grounding, structured outputs, parallelization, and caching. Also partnered with legal/compliance stakeholders at Nexteer Automotive to deliver an AI document comparison tool with traceability and confidence indicators, adopted by non-technical users and saving ~2 FTEs of review time.”
Prachi Jain
Mid-level Machine Learning Engineer specializing in NLP, LLMs, and MLOps
“Built and productionized a RAG-based analytics Q&A assistant for a financial analytics team, enabling natural-language querying across 200+ datasets (SQL tables, PDFs, compliance docs, wikis) and cutting turnaround time by 60%. Deep experience delivering regulated, audit-ready LLM systems on Azure (Azure OpenAI + LangChain) with strict grounding/citations, hybrid retrieval, and AKS-based low-latency deployment, plus strong collaboration with compliance analysts and auditors via iterative Gradio demos.”
Mid-level AI/ML Engineer specializing in NLP/LLMs and real-time data pipelines
Junior Machine Learning Engineer specializing in benchmarking, NLP, and computer vision
Mid-level AI/ML Engineer specializing in NLP, MLOps, and fraud detection
Mid-level AI/ML Engineer specializing in MLOps, NLP, and predictive modeling
Mid-level AI/ML Engineer specializing in NLP, LLMs, and MLOps in Financial Services
Mid-level AI/ML Engineer specializing in scalable ML systems and cloud MLOps
Mid-level AI/ML Engineer specializing in recommender systems, NLP, and MLOps
Pratik Jaiswal
Mid-level AI/ML Engineer specializing in financial services ML and MLOps
“ML engineer/data scientist with M&T Bank experience who built a production reinforcement-learning portfolio analytics tool for wealth management, emphasizing near real-time performance via batch/serving separation and robust generalization through stress-scenario backtesting and RL regularization. Strong MLOps background (Airflow, Grafana, MLflow) and proven ability to drive adoption with non-technical stakeholders using KPI alignment and SHAP-based explanations.”
Mid-level Machine Learning & Generative AI Engineer specializing in enterprise RAG and MLOps
Mid-level AI/ML Engineer specializing in NLP, MLOps, and compliance-focused ML systems
Mid-level AI/ML Engineer specializing in NLP, speech AI, and RAG systems