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Vetted Time Series Forecasting Professionals
Pre-screened and vetted.
Yogita Adari
Mid-level AI Engineer specializing in generative AI, multimodal evaluation, and agentic RAG systems
“Built and productionized an agentic LLM automation system for an insurance client to determine medication eligibility, using prompt-chaining plus a RAG pipeline over policy rules and deploying on AWS (Lambda/Step Functions, Bedrock) with a serverless architecture. Addressed major data/schema mismatch issues via a semantic matching pipeline and validated performance through human agreement scoring, A/B testing, KPI monitoring, and confidence-based human-in-the-loop review.”
Bhavana Anna
Mid-level AI/ML Engineer specializing in fraud detection and Generative AI (RAG)
“AI/ML engineer who has shipped production LLM and ML systems, including a RAG pipeline that ingested ~500k insurance/client documents to help adjusters answer questions faster and more consistently. Experienced in handling messy real-world document formats, tuning retrieval/chunking, and reducing latency via vector search optimization, precomputed embeddings, and caching. Also built orchestrated fraud-detection deployment workflows using AWS Step Functions and SageMaker, and partners closely with non-technical operations teams on NLP automation.”
Uttam Kumar
Intern AI Engineer specializing in LLM agents, RAG, and scalable cloud deployment
“AI/LLM engineer at GPT integrators who built a production multi-agent enterprise workflow integration system, tackling hard problems in agent orchestration, layered memory, and custom RAG over enterprise/user data. Also built an education-focused agent solution integrating with Canvas, Zoom, and email to automate classroom admin tasks, and is currently applying agentic AI to insurance underwriting workflows in collaboration with underwriters.”
Anvith Reddy T
Mid-level AI/ML Engineer specializing in Generative AI and MLOps
“ML/AI engineer with hands-on ownership of fraud detection and investigator-assist systems, combining anomaly detection with RAG-based LLM summarization in production. Stands out for translating research ideas into reliable cloud-deployed workflows that improved precision to 92%, cut review time by 25-30%, and increased investigator throughput by roughly 30% while also building reusable Python infrastructure for team-wide velocity.”
Chaitanya Kalagara
Mid-level Machine Learning Engineer specializing in LLMs, GenAI, and Computer Vision
“LLM/agent engineer who built a production multi-agent research automation system using LangGraph (planner, retriever with FAISS, supervisor, evaluator) with structured outputs and citation tracking for traceable reports. Emphasizes reliability and operations—LangSmith-based observability, multi-level testing, hallucination mitigation, and latency/cost controls—plus prior experience as a Computer Vision Software Engineer at Deepsight AI Labs working directly with non-technical customers.”
Alexander Conn
Principal Data Scientist specializing in cybersecurity ML and MLOps
“ML/NLP engineer (Beyond Identity) who built production semantic search and entity-resolution systems over internal security documentation, using LDA + BERT embeddings with FAISS/Pinecone to cut search time by 30%. Also scaled a real-time anomaly detection pipeline to millions of events/day with Spark and AWS Lambda, with strong emphasis on measurable validation (Precision@k, MRR, F1, ARI).”
Shruti Rawat
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps for financial services
“Built and deployed a production Llama 3-based RAG document Q&A system using FAISS, addressing context-window limits through chunking and keeping retrieval accurate by regularly refreshing embeddings. Has hands-on orchestration experience with LangChain and LlamaIndex for multi-step LLM workflows (including memory management) and collaborates with non-technical teams (e.g., marketing) to deliver AI solutions like recommendation systems.”
Swati Swati
Senior Data Scientist/Software Engineer specializing in ML systems and cloud DevOps
“AI software engineer with experience spanning LLM/RAG production systems and regulated fintech infrastructure. Built an end-to-end natural-language-to-SQL analytics assistant (Weaviate + GPT-4 + Supabase) shipped as an API with 92% accuracy and major time savings for non-technical users, and also owned demand-forecasting and CI/CD/containerization improvements for a Bank of America core banking deployment at Infosys.”
Alicia Geng
Entry-level AI/ML Engineer specializing in AWS MLOps and computer vision
“Built and shipped a production RAG question-answering system using LangChain/OpenAI, Docker, and FastAPI, then reduced hallucinations through disciplined retrieval tuning and constrained prompting. Also implemented a custom evaluation framework (QA-pair dataset) to measure faithfulness/relevance and deployed containerized ML microservices on AWS ECS/Fargate with ALB and rolling, zero-downtime updates.”
Sai Krishna Mallikanti
Mid-level AI & Data Scientist specializing in LLMs, RAG, and healthcare NLP
“Built a production LLM/RAG solution for healthcare operations teams to query large policy and care-guideline repositories in natural language. Improved domain alignment using vector retrieval plus parameter-efficient fine-tuning and prompt optimization, validated through internal user testing and metrics, cutting manual lookup time by ~40%. Also has hands-on experience orchestrating automated ML pipelines with Apache Airflow.”
Ruturaj Dixit
Junior Data Scientist specializing in AI/ML and product analytics
“Applied ML/data scientist who has owned backend-heavy AI systems end-to-end, including a market-signal platform on FastAPI/AWS and rapid MVP delivery in medical computer vision. Particularly interesting for teams needing someone who can combine model development, backend APIs, production debugging, and pragmatic low-latency architecture decisions.”
Vaishnavi K
Mid-level AI/ML Engineer specializing in GenAI, MLOps, and anomaly detection
“LLM/MLOps engineer who has shipped a production RAG-based technical documentation assistant (FastAPI) cutting manual review by 45%, with deep hands-on retrieval optimization in Pinecone/LangChain (HNSW, hybrid + multi-query search, caching). Also brings healthcare domain experience—building Airflow-orchestrated EHR pipelines and delivering FDA-auditability-friendly predictive maintenance solutions using SHAP/LIME explainability surfaced in Power BI.”
“Backend/data engineer who builds Python (FastAPI) data-processing API services for internal analytics/reporting, emphasizing modular architecture, async performance tuning, and reliability patterns (health checks, retries, observability). Also migrated legacy on-prem ETL pipelines to Azure using ADF/Data Lake/Functions and implemented a near-real-time ingestion flow with Event Hubs plus watermarking to handle late events and deduplication.”
Aman Jain
Mid-level Software Engineer specializing in cloud-native data pipelines and ML platforms
“Backend engineer who has owned end-to-end delivery of Python/FastAPI microservices for real-time data processing and alerting, including performance tuning (Postgres optimization, caching, async processing). Strong DevOps/GitOps background: Docker + Kubernetes deployments with GitHub Actions CI/CD and ArgoCD-driven GitOps, plus experience supporting phased on-prem to AWS migrations and building Kafka-based streaming pipelines.”
saran palle
Mid-level Applied AI Engineer specializing in agentic LLM workflows
“AI engineer with production experience building a LangGraph-based, stateful multi-agent system at MetLife to automate complex insurance claims adjudication, integrating document discovery, Azure Document Intelligence OCR/extraction, and health data analysis. Strong in agent orchestration and production deployment (Docker + FastAPI REST APIs), with a structured approach to reliability, evaluation, and stakeholder-driven requirements.”
Santhoshi Priya Sunchu
Mid-level Data Scientist specializing in NLP and predictive modeling
“AI/ML practitioner in healthcare/insurance (Blue Cross Blue Shield) who built and deployed a production NLP system to classify patient risk from unstructured clinical notes. Experienced in end-to-end pipeline orchestration (Airflow, AWS Step Functions/Lambda/SageMaker) and real-time optimization (BERT to DistilBERT on AWS GPUs), with strong clinician collaboration to drive adoption.”
Maheswar Mekala
Mid-level Machine Learning Engineer specializing in NLP, recommender systems, and MLOps
“ML/LLM engineer with production experience at General Motors building Transformer-based search and recommendation personalization for a high-traffic vehicle platform. Delivered significant KPI gains (17% conversion lift, 14% bounce-rate reduction) and optimized real-time inference via ONNX Runtime and INT8 quantization while implementing robust MLOps (Airflow/MLflow, monitoring, drift-triggered retraining) and stakeholder-facing explainability/dashboards.”
Surya Danturty
Intern AI/ML Engineer specializing in computer vision and time-series forecasting
“Undergrad who built a production RAG chatbot for a messy college website using OpenAI embeddings + FAISS, overcoming hard-to-crawl/non-selectable site content and strict API budget limits. Applies information-retrieval best practices (section-based chunking with overlap, precision/recall evaluation) and reliability techniques (edge-case testing, similarity thresholds, fallback responses), and has experience scaling similar indexing work to ~300,000 Wikipedia pages.”
Abhiroopsudansh Karengula
Mid-level AI/ML Engineer specializing in production ML, RAG systems, and MLOps
“Built and shipped a widely adopted, production-grade RAG internal search assistant that unified scattered engineering knowledge, deployed as a FastAPI service on Kubernetes with FAISS + LangChain. Demonstrates deep practical expertise in retrieval tuning (chunking, hybrid search, re-ranking) and in making LLM workflows reliable in production via guardrails, monitoring, and evaluation, plus strong cross-functional delivery with non-technical operations teams.”
Dharani Rasaputhra
Mid-level Data Analyst specializing in FinTech and e-commerce analytics
“Analytics professional with experience spanning financial services and e-commerce, including NAV reconciliation/reporting at State Street and revenue optimization work on checkout and retention. Stands out for combining strong SQL/Python data engineering with business-facing analytics, delivering measurable gains like 3-5% conversion improvement and 8-10% payment success uplift.”
SaiGanesh Konagalla
Mid-level ML Engineer specializing in NLP and Generative AI
“Healthcare AI/ML engineer with Epic experience who built and deployed a HIPAA-compliant GPT-4 RAG clinical assistant over large medical document sets, emphasizing privacy controls and low-latency performance. Also automated end-to-end retraining and deployment of patient risk models using orchestration/CI-CD (Jenkins, SageMaker, MLflow), cutting deployment time from hours to minutes while improving reliability.”
Dimple Galla
Mid-level Data Scientist / AI-ML Engineer specializing in RAG, MLOps, and real-time analytics
“Software/ML engineer who built a production automated job-finding and cold-email personalization system for Fortune 500 outreach, using JobSpy for dynamic scraping, LangChain orchestration, and LLM+vector DB semantic search with grounding/relevance metrics and guardrails. Also delivered a predictive investment analytics platform for financial advisors, communicating results via Tableau dashboards and portfolio KPIs like Sharpe ratio and drawdowns.”
Meghana Nandivada
Junior Machine Learning Engineer specializing in production ML systems and MLOps
“ML/AI engineer (TCS) who built and productionized a customer segmentation and personalized-offer recommendation pipeline end-to-end (data cleaning/feature engineering/clustering through Flask API deployment in Docker with monitoring). Emphasizes reliability and operational rigor via validation checks, periodic retraining, model/API versioning, and latency optimization, and has experience translating marketing KPIs into usable dashboards for non-technical teams.”
Ponugoti Sushma
Mid-level Machine Learning Engineer specializing in IoT, edge AI, and enterprise ML
“Built and productionized an LLM/RAG question-answering service over technical documentation, focusing on retrieval quality (reranking + IR metrics), latency, and scaling. Experienced orchestrating end-to-end ETL/ML workflows with Airflow/Prefect/AWS Step Functions and improving reliability via parallelism, retries, and shadow testing. Also delivered an explainable healthcare risk-flagging classifier with a stakeholder-friendly dashboard for a non-technical program manager.”