Senior AI/Data Engineer Needed to Build MVP (Entity Resolution + Risk Scoring) – 4 Week Project

@AwaisChaudhry

Hi there, I understand you want a production‑quality MVP for a financial intelligence platform focused on entity resolution and risk scoring, delivered in 4 weeks with independent work and clear, explainable results. I’ll build a compact, scalable stack that ingests CSV/API data, normalizes it, performs fuzzy or embedding‑based matching with confidence scores, and applies a simple, explainable risk model driven by flagged relations and graph proximity. The system will expose search and scoring APIs and include a minimal UI for lookup and reasoning, with clean docs and runnable deployment options (local or cloud). My approach: - Week 1: set data models, ingestion pipelines, and normalization; define schemas and endpoints. - Week 2: implement entity resolution with configurable similarity metrics (fuzzy and embeddings) and confidence scoring. - Week 3: build risk scoring logic, expose API, and ensure explainability of scores. - Week 4: finish a minimal UI/API, polish, tests, and deployment guidance. Tech stack I would use: Python, FastAPI for APIs, PostgreSQL for storage, a lightweight ETL layer, optional embedding service (OpenAI or sentence transformers), and optional graph support (Neo4j) if needed. The code will be clean, documented, and ready to run locally or on a chosen cloud. What are the primary data sources and delivery formats for ingestion (CSV, APIs) and any rate limits? What definitions of a risk score should we target, and what explainability requirements ex

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you’re looking to build a Minimum Viable Product focused on entity resolution and risk scoring. This involves creating a system that can effectively analyze and consolidate data while assessing associated risks. I’ve worked on several similar platforms and understand the key technical challenges involved. My experience with Python, PostgreSQL, and API development positions me well for this project. I’d be happy to go through the details and suggest the best technical approach. My plan would be to develop a robust ETL pipeline, ensuring seamless data integration, and implement entity resolution algorithms to meet your requirements. I have a few questions to get a better understanding: Q1 – What specific data sources do you plan to integrate for the entity resolution? Q2 – Are there particular risk scoring models or frameworks you prefer? Q3 – What user roles and permissions do you envision for the MVP? Looking forward to hearing from you.

@irsolutions

With my extensive experience in AI/data engineering, I understand the need for a skilled engineer to build a functional MVP for your financial intelligence platform. Your focus on entity resolution and risk detection aligns perfectly with my past projects in AI, data systems, and risk scoring. In previous engagements, I have successfully developed similar systems that required complex entity matching, fuzzy logic algorithms, and explainable risk scoring functionalities. My expertise in Python, FastAPI, large dataset handling, and PostgreSQL align with the non-negotiable skills you require for this project. I am confident in my ability to deliver a fully working MVP within the 4-week timeline, ensuring clean codebase documentation, API endpoints for search and scoring, and a functional UI. My approach would involve leveraging my experience in entity resolution and machine learning to create a scalable architecture while deploying on AWS for optimal performance. To move forward, I am ready to showcase examples of my previous work in AI and data systems, provide detailed insights into my MVP development approach, outline the tech stack I intend to use, and confirm my commitment to delivering within the given timeframe. Let's discuss how I can elevate your financial intelligence platform with my expertise.

@Herculesz

Hi, I’m a senior Python and AI/data engineer with experience building entity resolution and risk scoring systems, and I can deliver a clean MVP within your 4-week timeline. The main challenge is accurately matching entities across noisy datasets while keeping the system fast, explainable, and scalable. My solution is to build a FastAPI-based backend with a PostgreSQL database, using a hybrid approach for entity resolution—fuzzy matching (RapidFuzz) combined with embedding similarity (sentence-transformers) to generate confidence scores. For risk scoring, I’ll implement a transparent rule-based model with basic graph relationships (optionally Neo4j) to ensure explainability. Data ingestion will be handled via structured ETL pipelines, and I’ll expose search and scoring via API endpoints, with a minimal UI for querying and results visualization. The architecture will be modular and production-ready, with deployment on AWS or similar. I’ve built similar data pipelines and matching systems and can operate independently to deliver within 4 weeks. Thanks, Hercules

@nikhilgarg96

Hello, As a seasoned full-stack developer with a career spanning front-end and back-end technologies, I possess a unique blend of skills that align exceptionally well with the needs of your project. With an in-depth understanding and fluent expertise in Python, FastAPI, PostgreSQL, and ETL/data pipelines, I am confident in my ability to deliver a top-notch MVP meeting your specifications within the 4-week timeline. My proficiency also extends to building clean and scalable architectures on various cloud platforms like AWS and GCP, which is crucial for this project. My experience delving into AI encompasses entity resolution and fuzzy matching using techniques like embedding-based similarity, making me well-versed in the core aspect you seek for this platform. I can bring your goal of generating explainable risk scores from flagged entities to reality by leveraging my ML knowledge and designing scoring systems. Lastly, I'm not only invested in functionality but also usability. This translates into not only a working UI/API where users can search names/entities but also provides detailed information on matched entities along with their risk scores and reasoning - all contributing towards an intuitive user experience. Let's build this revolutionary financial intelligence platform together! With Regards!

@corpmember29

HELLO, I HAVE CAREFULLY REVIEWED YOUR REQUIREMENT FOR BUILDING AN ENTITY RESOLUTION AND RISK SCORING MVP. With 10+ years of experience in AI/data engineering, large-scale data systems, and search platforms, I can independently deliver a production-ready MVP within your strict 4-week timeline. APPROACH → I will design a modular pipeline using Python (FastAPI) with PostgreSQL for structured storage and optional Neo4j for relationship mapping. Data ingestion will normalize CSV/API inputs into a unified schema. Entity resolution will use a hybrid approach—fuzzy matching (RapidFuzz) + embedding similarity (sentence-transformers/OpenAI)—to generate confidence scores. Risk scoring will combine rule-based logic (flag matches) and graph proximity (entity relationships), with clear explainability for each score. A lightweight React UI or API interface will enable search, match visualization, and reasoning. STACK → Python (FastAPI), PostgreSQL, Redis (caching), Neo4j (optional), React (UI), Docker + AWS/GCP deployment. TIMELINE → Week 1: ingestion + schema | Week 2: entity resolution | Week 3: risk scoring + APIs | Week 4: UI, testing, deployment. I HAVE BUILT SIMILAR SYSTEMS INVOLVING DATA MATCHING, SEARCH, AND SCORING MODELS, AND CAN SHARE DETAILS DURING DISCUSSION. I CONFIRM I CAN DELIVER THIS MVP WITHIN 4 WEEKS. I WILL PROVIDE 2 YEAR FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE. WE WILL WORK WITH AGILE METHODOLOGY AND WILL GIVE YOU ASSISTANCE Thanks.

@devendrathakur12

Hello, Can we discuss about your entity resolution MVP project cause I have worked on systems that match messy entities and score risk using embeddings and graph logic. I can build ingestion, matching, scoring, and API using FastAPI, PostgreSQL, and sentence-transformers with a simple UI. How noisy are your datasets? Do you need real-time matching or batch first? Should risk scores be explainable per factor? I’ll keep matching modular so rules can evolve fast. Best regards, Devendra S.

@iosifpeterfi

I'm Iosif Peterfi, 15+ years leading security and data engineering initiatives across platforms, based in Berlin. This is my speciality: turning large datasets into trusted, explainable risk insights through robust entity resolution, scalable data pipelines, and governance-minded scoring. You're building a financial intelligence platform for entity resolution and risk detection across large datasets, with a 4-week MVP covering data ingestion, entity matching, risk scoring, and a search interface. I'll deliver a ready-to-run MVP with data ingestion, entity resolution returning confidence scores, a simple risk scoring mechanism with explainable reasoning, and a lightweight search API or UI so stakeholders can quickly assess matches and risk. Recently, I delivered a similar MVP for a financial data provider focused on customer data integration. We reduced manual review by 40% and improved match explainability, speeding risk assessment by days. Let's chat - I can walk you through my approach in 15 minutes.

@mubeenm5026

As a seasoned AI/Data engineer and the founder of Web Crest, I lead a team of 10 experts with over 10 years of experience building intelligent and scalable solutions from scratch - making us the perfect fit for your project. Web Crest specializes in precisely what you need: API Development using Python, advanced data engineering, and managing large datasets on PostgreSQL among other skills. We've successfully built similar systems involving entity resolution and risk scoring making use of APIs like FastAPI and Flask that rhyme perfectly with your requirements.

@OutsourceMan

⭐⭐⭐⭐⭐ As a Senior AI/Data Engineer with over 18 years of experience, I am very confident that my expertise is perfectly suited to building your MVP for the financial intelligence platform you envision. My extensive knowledge in Python, API development (especially using FastAPI), managing large datasets, and proficiency in PostgreSQL undoubtedly ticks several boxes off your requirement list. More specifically, I have vast experience in entity resolution and fuzzy matching. I understand how crucial it is to accurately match entities across datasets, particularly when it comes to names, companies, and aliases. Opening up possibilities for utilizing embedding-based similarity adds another layer of effectiveness to the system, which I’m well-versed in. And yes, I always ensure that my codebase is organized and well-documented. Regarding data infrastructure, I have hands-on experience with PostgreSQL and building ETL/data pipelines. An agile deployment that can scale naturally is essential for an MVP of this nature and falls within my range of design skills. While working with AWS or GCP is second nature to me, I haven't dabbled much with Neo4j but would be excited to get acquainted with it through this project if needed. Lastly, through leveraging my past experience in similar domains, I will deliver a working MVP as per your specifications within the 4-week period - per the timeline you envisioned.

@purplemaestro

Hi. I'm a Senior AI/Data Engineer with proven expertise in entity resolution and risk scoring systems. I understand the MVP scope and can deliver a robust solution within your 4-week timeline, prioritizing clarity on your technical requirements upfront. My approach will focus on building a scalable foundation that balances accuracy and performance based on your specific use case. I'm prepared to discuss the clarification questions (fuzzy matching preferences, multilingual support, accuracy vs. speed trade-offs) to ensure alignment before we begin. I'm confident I can deliver high-quality results and am available to start immediately.

@Hubready

Hello! I am a US-based senior software engineer with extensive experience in AI and data engineering, and I’m excited about your project to build an MVP for entity resolution and risk scoring. I carefully read the project description and have a solid understanding of your needs. With over 15 years in the industry, I specialize in Python, PostgreSQL, and API development, ensuring that the solutions I deliver are both technically robust and aligned with your business goals. To clarify a few points and better tailor my approach, could you please clarify the following questions to help me better understand the project? 1. What specific data sources will be utilized in the entity resolution process? 2. Are there any particular risk scoring models you have in mind, or should I leverage existing frameworks? My strategy for success will involve defining clear milestones, developing a robust ETL pipeline, and creating a user-friendly API for seamless integration. I have a proven track record with similar projects, such as building data processing tools and AI-driven dashboards for various clients. I am committed to delivering production-ready results and ensuring your project exceeds expectations. I look forward to discussing how we can make this project a success. Best, James Zappi

@widuIT

Hi, this MVP is very doable in 4 weeks if it is treated as a focused entity-resolution and explainable risk engine, not as a full intelligence platform. My approach would be: first build a clean ingestion and normalization layer for CSV/API sources, then implement entity resolution using a hybrid strategy (deterministic normalization + fuzzy matching + embedding similarity), and finally add a simple risk engine that scores entities based on flagged matches and basic relationship proximity, always returning the reasoning behind the score. For the stack, I would use Python with FastAPI, PostgreSQL, pgvector for similarity search, and sentence-transformer embeddings, with a minimal search UI on top. That keeps the system lean, fast to build, and still scalable enough for the next stage. Relevant work on my side includes data-heavy backend systems, financial/operational workflows, and LLM-assisted platforms where traceability, structured outputs, and explainable logic mattered more than flashy AI claims. I can deliver this in 4 weeks, structured as: week 1 ingestion/schema, week 2 resolution pipeline, week 3 risk scoring/API, week 4 UI, deployment, QA, and documentation. Nico – widuIT - Top Freelancer LATAM

@ivaylo8

Hello, I’m Ivaylo, a senior AI/Data Engineer with a track record building production-grade data platforms for ML and risk-scoring workloads. I will deliver a concise, scalable MVP in 4 weeks that covers ingestion, entity resolution with fuzzy or embedding-based matching and confidence scores, explainable risk scoring, and a clean API + minimal UI. The approach is pragmatic: establish a robust data model and ETL pipeline, implement a modular entity-resolution service with tunable thresholds, and expose search and scoring via FastAPI endpoints backed by PostgreSQL with optional graph relationships. I’ll ensure traceability, tests, and documentation so you can evaluated, extended, and deployed quickly. Why this approach works for a Palantir-like MVP: simple yet scalable architecture, production-ready code, clear scoring rationale, and fast feedback loops for iteration. I’m ready to start immediately and can accommodate your data sources (CSV/API) and any AML/KYC nuances you require. Best regards, Ivaylo

@maximg10

Hello. Thanks for your job posting. ⭐Senior AI/Data Engineer Needed to Build MVP (Entity Resolution + Risk Scoring) – 4 Week Project⭐ I'm the developer you're looking for. I can successfully complete your project. Let's chat for a more detailed discussion. Thank you. Maxim

@Feriver

Hello, I have reviewed your project requirements for a financial intelligence MVP focused on entity resolution and risk scoring. I have extensive experience building data pipelines, entity matching systems, embedding-based similarity models, and risk scoring mechanisms using Python, FastAPI, PostgreSQL, and graph databases. My approach ensures clean, modular code with explainable scoring and a functional search interface. I can deliver a working MVP within 4 weeks following your timeline: Week 1 – data ingestion and schema; Week 2 – entity resolution; Week 3 – risk scoring and API; Week 4 – UI, deployment, and polish. I will implement fuzzy matching and embedding-based entity resolution, generate confidence scores, and provide a minimal yet functional UI for search and risk visualization. The codebase will be fully documented, with clear API endpoints and deployment-ready architecture, ensuring scalability and maintainability. I can confirm that I can deliver this project in 4 weeks with production-quality results, fully tested and explainable. Thanks, Asif

@hotanloc

I am a senior AI/Data Engineer with extensive experience building entity resolution and risk-scoring systems for financial intelligence. To deliver your MVP in four weeks, I will use Python and FastAPI to build a high-performance backend, backed by PostgreSQL and pgvector for accurate, embedding-based fuzzy matching. During week one, I will architect a scalable ETL pipeline to normalize and ingest your datasets effectively. By week two, I will implement the entity resolution engine, followed by an explainable, graph-based risk-scoring logic in week three. I will dedicate the final week to developing the search UI, API documentation, and deploying the solution to a cloud environment. My background in KYC and AML systems allows me to hit the ground running with complex, real-world datasets. I am highly comfortable working independently and delivering production-quality, documented code on tight deadlines. I have attached examples of similar search and data-matching systems I have successfully deployed in the past. I confirm that I am available to start immediately and will meet your four-week delivery timeline without compromise. I look forward to discussing the architecture and schema design with you today.

@maxscender

I can help you build this. A few technical considerations that will shape the architecture: Entity resolution at scale has a blocking problem. Comparing every entity pair is O(n²). I'd implement locality-sensitive hashing or phonetic blocking (Metaphone) to reduce candidate pairs before running embedding similarity. This keeps it fast without sacrificing match quality. For embeddings: Sentence transformers (all-MiniLM-L6-v2) will outperform OpenAI embeddings for entity matching while being free and faster. Fine-tuning isn't needed for MVP but the architecture should allow it later. Risk scoring explainability: I'd structure this as a weighted feature breakdown (flagged entity match: +40, 2nd-degree connection to flagged: +15, etc.) rather than a black-box ML model. Users can actually audit decisions. Stack: FastAPI, PostgreSQL with pg_trgm extension for fuzzy search, Redis for caching resolved entities, simple React frontend. Neo4j only if relationship traversal becomes a bottleneck—PostgreSQL recursive CTEs handle basic graph queries fine for MVP. The deliverable would be a containerized system deployable anywhere, not tied to specific cloud infrastructure.

@Julian29

HELLO, I HAVE 10+ YEARS OF EXPERIENCE IN AI/DATA ENGINEERING, BUILDING SCALABLE SYSTEMS FOR ENTITY RESOLUTION, SEARCH, AND RISK ANALYTICS USING PYTHON, FASTAPI, AND MODERN DATA STACKS. APPROACH: I WILL DESIGN A MODULAR MVP WITH DATA INGESTION (CSV/API → ETL → POSTGRES), ENTITY RESOLUTION USING HYBRID FUZZY MATCHING + EMBEDDINGS (SENTENCE TRANSFORMERS), AND A TRANSPARENT RISK SCORING LAYER USING RULES + GRAPH-BASED RELATIONSHIPS (OPTIONAL NEO4J). A FASTAPI BACKEND WILL EXPOSE SEARCH + SCORING APIS WITH A LIGHTWEIGHT UI FOR QUERYING AND RESULTS VISUALIZATION. TECH STACK: PYTHON, FASTAPI, POSTGRES, PANDAS, SENTENCE-TRANSFORMERS/OPENAI EMBEDDINGS, OPTIONAL NEO4J, DEPLOYMENT ON AWS/GCP. I HAVE BUILT SIMILAR DATA PIPELINES, MATCHING SYSTEMS, AND SEARCH-BASED APPLICATIONS WITH CLEAN, DOCUMENTED CODE AND PRODUCTION-READY ARCHITECTURE. CONFIRMATION: I CAN DELIVER A FULLY FUNCTIONAL MVP WITHIN 4 WEEKS AS PER YOUR MILESTONES. I WILL PROVIDE 2 YEAR FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE, WE WILL WORK WITH AGILE METHODOLOGY AND WILL GIVE YOU ASSISTANCE FROM ZERO TO DEPLOYMENT. I EAGERLY AWAIT YOUR POSITIVE RESPONSE. THANKS