Vetted Machine Learning Professionals

“ML/NLP engineer with Industrial IoT experience who built an end-to-end anomaly detection and GenAI explanation system: AWS (S3, PySpark, EC2/Lambda) pipelines feeding dashboards, plus transformer-embedding vector search to connect anomalies to noisy maintenance notes and past events. Demonstrated measurable impact (15% lift in defect detection; ~35% reduction in manual review; 35% fewer preprocessing errors) and strong productionization practices (orchestration, monitoring, rollback, data-quality controls).”

“Backend/data engineer with strong AWS production experience spanning serverless APIs and containerized workers (Lambda, API Gateway, ECS) plus data pipelines (Glue, S3, Athena/Redshift). Has modernized legacy SAS/cron batch systems into Python/AWS with parallel-run parity validation and low-risk cutovers, and has owned ETL incidents end-to-end (CloudWatch detection, backfills, and preventative controls). Targeting $130k–$150k base and strongly prefers remote, with occasional Bethesda onsite acceptable.”

“CTO-level leader who drove a major post-buyout transformation at NPact—modernizing engineering (CI/CD, QA, observability), moving products toward SaaS/cloud, and scaling the org from ~20 to ~70 while maintaining 97% retention. Uses instrumentation and workflow analytics (including Atlassian-derived data) to improve delivery, citing an ~80% reduction in feature/bug churn through better scoping and requirements. Comfortable with board-level ROI decisions and customer/fundraising conversations, translating technical tradeoffs into clear business outcomes.”

“ML/NLP engineer focused on large-scale data linking for e-commerce-style catalogs and customer records, combining transformer embeddings (BERT/Sentence-BERT), NER, and FAISS-based vector search. Has delivered measurable lifts (e.g., +30% matching accuracy, Precision@10 62%→84%) and built production-grade, scalable pipelines in Airflow/PySpark with strong data quality and schema-drift handling.”

“AI/LLM practitioner who has deployed production chatbots across e-commerce, HRMS, and real estate, focusing on retrieval-first workflows for factual tasks like product and property search. Optimized intent understanding and significantly improved latency by using lightweight embeddings and tuning the inference pipeline on Groq (Llama 3.3), while applying modular orchestration and measurable production evaluation.”

“Full-stack product engineer who has shipped customer-facing features end-to-end, including a product detail page backed by Java/Spring Boot microservices and a React/TypeScript UI. Demonstrated measurable impact through performance and maintainability improvements (30% faster APIs, 25% less duplicated UI code, 40% reduced API complexity via GraphQL) and has operated/scaled apps on AWS with CI/CD, monitoring, and incident-driven scaling fixes.”

Sachin Kulkarni

Screened

Mid-level AI/ML Engineer specializing in LLMs, NLP, and AWS MLOps

New York, US3y exp

“Recent master’s graduate in robotics with applied experience across reinforcement learning and ROS 2 autonomy stacks. Built an RL-based drone vertiport traffic controller (PPO) focused on reward design and simulation integration, and has hands-on navigation work in ROS 2 including LiDAR preprocessing, SLAM/path planning, and stabilizing TurtleBot3 wall-following. Also brings deployment experience containerizing robotics nodes and scaling them with Kubernetes on AWS.”

Sai Kumar K

Screened

Mid-Level Frontend Engineer specializing in legal data platforms

5y exp

“Built and operated end-to-end legal-document data pipelines fed by hundreds of scraper sources, emphasizing data quality validation, reliability (CloudWatch monitoring/alerting, retries, backfills), and serving enriched legal data via serverless AWS APIs (Lambda/API Gateway). Experienced in keeping API contracts stable with additive versioning practices and shipping MVPs quickly with CI/CD and observability in place.”

Tarun Gowda

Screened

Mid-level AI Engineer specializing in Generative AI and multimodal RAG

Morristown, NJ3y exp

“Full-stack engineer who helped build and launch an internal genAI platform called GAIL, supporting multiple LLMs, confidential document upload for RAG pipelines, and collaborative chat. Worked across FastAPI, React/TypeScript, AWS/DynamoDB, and Azure, with notable ownership of backend RAG logic, MCP integration architecture, and frontend fixes that improved chat usability.”