.png){kind=logo}
Vetted TensorFlow Professionals
Pre-screened and vetted.
Anishkumar Mahalingam Iyer
Intern Software Engineer specializing in AI/ML infrastructure and applied machine learning
“Interned at Rivian where they built and deployed a production Whisper-based ASR + LLM real-time event labeling pipeline to help autonomous-vehicle engineers diagnose failures and route issues to triage teams. Also built a stateful multi-agent "Code Partner" developer assistant using LangGraph/LangChain (planner/router/coder/critique/tester) with evaluation, adversarial testing, and stakeholder-friendly communication practices.”
Ahmed Atallah
Senior Controls & Localization Engineer specializing in autonomy, sensor fusion, and MPC
“Robotics software engineer focused on state estimation and localization reliability, with deep hands-on EKF tuning/validation using DGPS ground truth and integrity-risk-based uncertainty calibration. Built middleware-agnostic interfaces with ROS wrappers to enable repeatable ROS bag playback testing, and implemented CI at Caterpillar to automatically build the localization stack and run unit tests plus bag-based regressions before merge.”
Atyab Hakeem
Junior Data Scientist / ML Engineer specializing in GenAI and computer vision
“Software engineer who built and deployed OddPulse, a multi-agent LLM-powered continuous financial auditing system aimed at reducing compliance penalties by catching issues before audit cycles. Experienced with TrueAI-based agent orchestration, Airflow on GCP batch workflows, and rigorous evaluation/benchmarking (hit rate/MRR, latency/TTFT, cost) alongside security controls for sensitive financial data.”
Sudhan Louis
Director of Enterprise Architecture specializing in digital transformation, AI, and API strategy
“Hands-on architect/technology leader who builds prototypes (including Agentic AI wellness/biomarkers) and then scales teams to execute. Led a ~$400M global e-commerce transformation spanning 95 countries with active-active US/EU multi-region resilience, microservices/MFE (MACH), and strong security patterns (service mesh + API gateway + Ping Identity), plus modern data foundations (customer hub/MDM/Snowflake, data fabric/medallion).”
Mason Acevedo
Mid-Level Software Engineer specializing in data pipelines, APIs, and ML
“Software engineer whose recent work includes co-designing and building a "Shared Profile" feature for a social event-planning app (Again, Sometime). Previously at Pure Storage, set up Docker-standardized Ubuntu/Python environments to simulate hardware testbeds and support workload/performance regression testing for other engineering teams; no robotics/ROS experience.”
Snehitha Borra
Mid-level Data Engineer specializing in cloud data platforms and big data pipelines
“Healthcare data engineer with hands-on ownership of claims/member data pipelines on a cloud analytics platform, spanning batch and streaming ingestion (Airflow/Kafka/Spark/Databricks) through serving for reporting. Emphasizes reliability and data quality via embedded validation, schema-drift detection, deduplication, and operational monitoring/incident response, plus pragmatic CI/CD and observability setup in early-stage/ambiguous projects.”
Jayanti Lahoti
Junior Full-Stack Software Engineer specializing in AI and cloud-native systems
“Backend/systems-oriented engineer focused on building production-constrained LLM agent workflows that automate repetitive operator tasks via intent/entity extraction, retrieval grounding, and structured action recommendations with human-in-the-loop review. Emphasizes reliability through deterministic orchestration, strict tool/function schemas, observability, and disciplined evaluation/feedback loops, with strong experience handling messy multi-service operational data and idempotent execution.”
Krishna Kodur
Mid-level Robotics & AI Researcher specializing in human-robot interaction and reinforcement learning
“Robotics software engineer who built an end-to-end mobile manipulation platform (Franka Panda on a Clearpath Ridgeback) for a simulated-kitchen human-robot interaction study with natural speech commands, implemented in Python/ROS. Has hands-on experience integrating diverse sensors (RealSense, LiDAR, biosignals) with deep learning frameworks (PyTorch, Hugging Face) and fine-tuning GPT-Neo, plus simulation (Gazebo) and modern deployment practices (Docker/Kubernetes, CI/CD).”
jiayu Zhao
Junior Quantitative Analyst and Full-Stack Engineer specializing in FinTech and web platforms
“Backend/distributed-systems engineer with AI infrastructure experience who built an AI-driven video generation platform, focusing on an asynchronous FastAPI-based orchestration layer between user APIs and heavy inference services. Strong in production instrumentation and latency/concurrency optimization; actively learning ROS 2 but has not yet worked on physical robotics or ROS-based deployments.”
Shram Kadia
Mid-level Software Engineer specializing in backend systems, cloud-native apps, and AI platforms
“Backend/full-stack engineer who has owned production systems end-to-end, including a Dockerized Node.js/TypeScript probabilistic fault-tree analysis service for nuclear safety research deployed on AWS. Also built and operated a FastAPI-based RAG pipeline over 200+ PDFs using FAISS, focusing on low-latency, idempotent workflows and strong observability; experienced with API design and Playwright E2E automation across React/Angular projects.”
Mathew Martin
Mid-level Software Engineer specializing in cloud data platforms and LLM applications
“LLM/agent builder with experience shipping production LLM features at an early-stage ed-tech mental wellness startup (conversation analysis + structured feedback via FastAPI, OpenAI API, Render, CI/CD). Also built a multi-step dining concierge agent using OpenSearch over Yelp data with fallback query relaxation, and has enterprise data engineering experience at Capgemini migrating databases to Snowflake with robust ETL normalization and data-quality handling.”
Zhiwen Zhao
Junior Data Engineer specializing in cloud ETL and big data platforms
“Data engineer focused on transit/transportation datasets, building Spark-based pipelines that ingest from Oracle/APIs, apply PySpark data-quality fixes, and publish star-schema fact tables to Azure Data Lake. Experienced troubleshooting complex Spark failures (using checkpointing to manage long lineage) and operating Airflow-driven backfills and GitLab CI deployments for production DAGs.”
Yash Rangucha
Mid-level Software Engineer specializing in backend microservices and real-time streaming
“Built and owned an end-to-end LLM-powered enterprise retrieval pipeline at ServiceNow, spanning ingestion of structured/semi-structured sources through vector retrieval and real-time API serving. Focused heavily on reliability and quality (multi-stage validation, monitoring, evaluation pipelines) while also driving performance improvements (~35% faster responses) via caching, async processing, and SQL/query optimization.”
Ansh Parikh
Junior Software Engineer specializing in full-stack and machine learning
“Full-stack web developer with experience owning products from client discovery through launch and post-launch iteration, including a complete freelance build for an interior design firm and a large-scale React/TypeScript migration during an internship at Gateway Ticketing Systems. Stands out for balancing strong visual design with performance and SEO, and for improving emergency-use UX in an MVP product through flow simplification and A/B testing.”
Yeshwanth Vudugu
Mid-level Data Analyst specializing in banking and product analytics
“Analytics engineer/data analyst with Bank of America experience turning fragmented financial data across SQL Server, PostgreSQL, Kafka, and flat files into trusted Snowflake/dbt reporting models. Stands out for unifying disputed business definitions like churn and payment success rate, automating manual analysis in Python, and pairing strong data quality rigor with stakeholder adoption through self-service dashboards.”
Veselin Yovchev
Senior Unity Developer specializing in game development and interactive media
“Unity/C# game developer with hands-on experience shipping lightweight, analytics-driven gameplay systems across WebGL, multiplayer, VR, and mobile. Particularly notable for building a cloud-backed character builder with asset-bundle streaming and cross-device persistence, and for creating reusable level design tooling that improved design speed by 2.5x.”
Vamsi Reddy
Mid-level AI/ML Engineer specializing in healthcare and financial ML systems
“ML/AI engineer with hands-on experience shipping both predictive healthcare models and clinical GenAI assistants into production. They combine strong MLOps depth across Azure and AWS with healthcare-specific safety thinking, including PHI guardrails, retrieval grounding, and production monitoring, and they also built internal Python tooling for fraud ML workflows at Capital One.”
Animan Khandelwal
Entry-level ML Engineer specializing in multimodal AI and healthcare applications
“Backend/ML engineer who built and operated a production WhatsApp assistant end-to-end using a modern RAG stack, delivering >90% automation with sub-2-second latency. Shows strong depth in retrieval quality, observability, evaluation, and incident handling, and has also applied similar AI workflow patterns to a clinical diagnostic assistant processing medical PDFs.”
Hari Shankar Raghuraman
Mid-level AI/ML Engineer specializing in scalable ML, NLP, and MLOps
“ML/AI engineer with strong production depth across classical ML, MLOps, LLM/RAG, and scalable Python data platforms, with experience at Cisco and Accenture. Stands out for tying technical decisions to measurable business outcomes, including $1.2M annual savings, 40% faster support resolution, and broad internal adoption of shared engineering frameworks.”
sri venkata naga jahnavi Bandla
Mid-level AI/ML Engineer specializing in cybersecurity and fraud analytics
“AI/ML engineer with production experience across both classical ML and Generative AI, including a real-time banking fraud detection platform at Deloitte and a RAG-based cybersecurity threat analysis feature at Accenture. Stands out for owning systems end-to-end—from feature pipelines and model tuning through deployment, monitoring, retraining, and API/platform reliability—with measurable impact on fraud accuracy, false positives, and SOC analyst efficiency.”
Saumya Pailwan
Junior Software Engineer specializing in applied AI and audio ML
“Engineer with unusually mature experience leading AI-assisted development, including orchestrating multiple coding agents across a data pipeline feature as if managing a small engineering team. Stands out for balancing aggressive adoption of AI tools with disciplined judgment around architecture, security, and merge quality, and for translating that experience into stronger tech leadership.”
venu madhav
Mid-level AI/ML Engineer specializing in Generative AI and MLOps
“AI engineer and current tech lead building a RAG-based multi-agent QA platform for financial document analysis at significant scale (40,000-50,000 documents). They combine Python, CrewAI, FastAPI, Hugging Face embeddings, Pinecone, and AWS SageMaker to deliver retrieval, calculation, summarization, forecasting, and visualization workflows, while leading a small cross-functional team.”
CLINT DMELLO
Mid-level SRE/DevOps Engineer specializing in cloud infrastructure and Kubernetes
“Full-stack engineer who has owned an AI-powered HTTP monitoring dashboard end to end, from Node.js/MongoDB backend and dashboard UI through deployment and reliability controls. Particularly strong in turning raw technical signals into usable AI-assisted product experiences, with concrete impact including ~60% faster anomaly detection and meaningful AI cost optimization.”
Can Karakoc
Intern Software Engineer specializing in AI, data pipelines, and full-stack systems
“Candidate has built multiple zero-to-one AI/full-stack products spanning bioinformatics search, rental marketplace semantic search, and an SDR agent for a hospitality startup. Particularly strong at turning LLM/embedding concepts into usable products with modular workflows, explainable outputs, and production-minded infrastructure.”