Vetted Reinforcement Learning Professionals

“Built and deployed an Azure-based, fine-tuned CLIP visual retrieval system at Staples for a ~300k-item product catalog, improving edge-case recall by 12% by engineering a custom delta-similarity/dynamic-margin loss. Also has robotics experience using ROS2 for sensor/compute orchestration, including GPS-time-synchronized sensor triggering for robot swarms and latency-bounded optical-flow benchmarking for edge deployment.”

Ashwith Poojary

Screened ReferencesStrong rec.

Mid-level Software & AI Engineer specializing in Robotics, LLMs, and Reinforcement Learning

4y exp

“Robotics/AI Master's thesis researcher building an LLM-driven workflow to generate and evaluate robot policies before running them in an environment. Also built a local LLM-based real-time target-tracking robot using a pan-tilt camera with LangChain + Ollama, and has hands-on ROS 2/Gazebo experience including URDF-based simulation and a TurtleBot multi-agent chase project.”

suparshwa patil

Screened ReferencesStrong rec.

Mid-level Software Engineer specializing in AI platforms and full-stack systems

Santa Clara, CA4y exp

“Built and shipped a production AI-powered Q&A/RAG onboarding assistant at One Community Global that unified knowledge across Notion, Google Docs, and Slack, cutting volunteer onboarding time by 45%. Demonstrates strong end-to-end ownership: LangChain agent orchestration integrated into a FastAPI backend, rigorous evaluation (200-query dataset, ~85% accuracy), and production feedback/monitoring with source-attributed answers to build user trust.”

“Backend/ML engineer who has shipped high-scale real-time systems across e-commerce and healthcare: built a PharmEasy real-time recommendation engine for ~2M monthly users (cut feature latency 5 min→30 sec; +15% cross-sell) and architected a HIPAA-compliant multimodal clinical diagnostic workflow (DICOM+EHR) with XAI, MLOps (MLflow/Airflow/K8s), and drift/monitoring guardrails supporting 10k+ daily predictions.”

“Robotics software engineer/researcher focused on perception, SLAM, and sensor fusion, with hands-on experience taking systems from simulation to embedded/real-time deployment. Led transparent-surface (glass) detection using GDNet and achieved a major real-time speedup (~7–9 FPS to ~30 FPS) while preserving >90% recall, and has built ROS-based EKF GPS-IMU fusion plus profiled/optimized Visual SLAM for performance and memory stability. Also brings production-style deployment skills via Docker/Kubernetes orchestration of ML inference services with autoscaling and model update rollouts.”

“Master’s student and Deep Learning teaching assistant who teaches LLM/VLM fine-tuning (including LoRA) and built a Hugging Face LLM fine-tuned for unit conversion, improving reliability by analyzing synthetic data and filling missing number-system conversion examples. Also implemented the Raft consensus protocol using gRPC in a distributed systems course with correctness validated by unit tests.”

“LLM/ML engineer who has shipped an enterprise RAG-based Q&A system (LangChain/LlamaIndex, FAISS + Azure Cognitive Search, GPT-3.5/4 via OpenAI/Azure OpenAI) to production on Docker + Kubernetes/OpenShift, tackling hallucinations, retrieval quality, latency/cost, and RBAC/IAM security. Also partnered with operations leaders to turn manual reporting into an LLM-powered summarization and forecasting dashboard driven by real KPIs and iterative stakeholder feedback.”

“Built a production real-time anomaly detection platform for high-frequency trading at HSBC, using a streaming stack (Pulsar + Spark Structured Streaming + AWS Lambda) and a transformer-based model combining time-series and numerical signals. Experienced in MLOps and safe deployment (Kubernetes, canary releases, MLflow/Grafana monitoring) and in aligning model performance with risk/compliance expectations through SLA-driven tuning and stakeholder-friendly dashboards.”

“Co-founded a healthcare AI startup building and deploying software directly with end users, emphasizing rapid shipping, deep user interviews, and workflow-first adoption. Has hands-on production deployment experience on AWS (including diagnosing a silent AWS App Runner failure caused by an ARM vs amd64 Docker build mismatch) and is motivated by customer-facing, travel-heavy roles to keep engineering tightly connected to real-world usage.”

“Applied NLP/ML in healthcare and financial services, including fine-tuning BERT on unstructured EHR text and building embedding-based similarity search for clinical concepts. Also redesigned a Wells Fargo fraud detection data pipeline using modular Python + AWS Glue/Step Functions, cutting runtime ~40% with improved monitoring and reliability.”

Nikitha Kommidi

Screened

Mid-level AI/ML Engineer specializing in fraud detection, NLP, and MLOps

6y exp

“Built a production real-time fraud detection and customer-support automation platform at Citibank, tackling extreme class imbalance (reported ~1:5000) and strict latency constraints. Combines hands-on MLOps (Airflow, Kubernetes, MLflow; Snowflake/Spark/S3 integrations; CI/CD model promotion) with cross-functional delivery to Risk & Compliance focused on interpretability and reducing false positives.”

ASHISH DONDAPATI

Screened

Mid-level AI/ML Engineer specializing in Generative AI for Financial Services

San Francisco, USA6y exp

“ML/AI engineer with strong financial-services domain experience who has built production systems spanning trade anomaly detection, investment-research RAG, and agentic LLM workflows. Particularly compelling for teams needing someone who can take ML/GenAI from prototype to monitored production while balancing compliance, latency, cost, and reliability.”

“Built and owned an enterprise LLM/RAG document intelligence platform for PNC Financial Services in a compliance-heavy environment, focused on grounded answers over internal finance and policy documents. Stands out for combining GenAI product delivery with production engineering discipline, delivering 60% faster document review and materially better answer quality while creating reusable FastAPI-based AI services for multiple teams.”

“Robotics software engineer focused on ROS 2 localization/SLAM: built a particle-filter (Monte Carlo) localization system in Python with likelihood-field modeling to handle noisy LiDAR and dynamic environments. Strong in debugging ROS 2 integration issues (tf2 frame sync, DDS/QoS message reliability) and in profiling/optimizing pipelines to reach real-time performance (~10 Hz) using precomputation and KD-trees.”

“AI engineer who deployed a production LLM-powered safety system for an education platform, combining rule-based checks, multi-LLM verification, and selective context (prompt+image vs image-only) to prevent explicit prompts/images from getting through. Strong focus on reliability via benchmarking, trace-based failure analysis, and continuous improvement driven by stakeholder feedback and manual review.”

“AI/ML engineer with healthcare domain depth who led a HIPAA-compliant, production LLM system at McKesson to automate clinical document understanding—extracting entities, summarizing provider notes, and supporting authorization decisions. Hands-on across Spark/Python ETL, Hugging Face + LoRA/QLoRA fine-tuning, RAG, and cloud-native MLOps (Airflow/Kubernetes/Step Functions, MLflow, blue-green on EKS/GKE), with explicit work on PHI handling and hallucination reduction.”

“Built and scaled a multi-agent LLM automation pipeline during a fintech internship, growing from a rapid 1-week proof-of-concept to a 15+ agent hierarchical system that cut market brief report generation time from ~5 hours to under 30 minutes. Hands-on with agent frameworks (Haystack, CrewAI, LangChain) and experienced in debugging agent communication issues via sandboxed modular testing and context/token management; also regularly gives architecture-first technical demos at multiple hackathons and university events.”

Jaideep bommidi

Screened

Senior ML Engineer & Data Scientist specializing in LLM agents, retrieval/ranking, and MLOps

Denton, TX8y exp

“Machine Learning Engineer currently at Webster Bank building an enterprise-scale LLM agent for Temenos Journey Manager/Maestro, using RAG-style multi-stage retrieval with FAISS/Pinecone, hybrid dense+sparse search, and LoRA fine-tuning optimized via NDCG/MAP and A/B testing. Previously handled messy incident/telemetry data at Deuta Werke GmbH with deterministic + fuzzy entity resolution, and has strong production data engineering experience across Spark/Hadoop and Python ETL systems.”

ESHWANTH D. G

Screened

Mid-level Robotics Software Engineer specializing in autonomous perception and sensor fusion

CA, USA4y exp

“Robotics engineer with Honeywell and Tata Motors experience deploying ROS/ROS2 autonomous mobile robot fleets into live factory environments, integrating sensors, safety PLCs, and on-prem services. Known for solving end-to-end latency and stability issues (including network spikes under load) using gRPC, Docker, and improved diagnostics—cutting diagnosis time from hours to minutes and achieving sub-150 ms control response.”