New

Data & AI Platform Engineer

Spectraforce Technologies
United States, New York, New York
Nov 06, 2025
Role: Data & AI Platform Engineer Location: Complete Remote Duration: 5 +Months (Poss. Of Extension) Overview for Supplier The client is the venture capital arm of company with a mature data/analytics environment (AWS-based, Python-first) that is now adding AI/LLM-enabled capabilities for investment and platform teams. The successful candidate must be able to work in a small environment (few engineers, high autonomy) and must be comfortable taking relatively unstructured business requirements and producing production-grade solutions. Required/ Must have skills: 1. Proven hands-on Python, SQL, and AWS experience in a low-oversight context (not advisory-only). 2. Experience delivering AI-/LLM-backed internal tools or workflows. 3. Experience with Slack APIs and frameworks. 4. Experience with and/or ability to build internal applications in Retool. 5. Experience with Salesforce APIs. 6. Experience with Venture Capital firms. The ideal candidate would have affirmative experience in each of the six areas above. Role Summary This role will contribute to data and AI-enabled internal products for a venture capital firm. The primary emphasis is on next-generation capabilities (LLM-assisted sourcing and diligence, enrichment, search/retrieval, internal research tools), delivered on top of the existing AWS/Python stack and surfaced through Slack, Retool and Salesforce CRM. The role will require working within enterprise engineering and security guidelines. Ongoing Projects AI- and LLM-enabled solutions - Design and build AI-assisted workflows for investment, diligence, and portfolio monitoring (e.g. document and meeting summarization, entity extraction, classification, similarity/"companies like this" features). - Integrate with major LLM providers and retrieval/vector services; establish patterns for prompt design, evaluation, and monitoring. - Expose AI capabilities through internal UIs (Retool) or lightweight APIs for non-technical teams. Data and enrichment pipelines (Python + AWS) - Develop Python-based ingestion and transformation jobs that run on AWS (S3, Lambda, ECS, Aurora Postgres). - Implement reusable patterns for onboarding new external data sources (commercial data, 3rd-party APIs, portfolio-company data). - Prepare and serve data in formats suitable for AI/LLM workloads (embeddings, indexes, features). Internal applications - Build and maintain Retool applications to support day-to-day investment and platform operations (deal views, company dossiers, data quality, AI results review). - Ensure applications meet basic standards for access control, logging, and performance. Core integrations - Keep Salesforce CRM aligned with the central data store to ensure core entities (company, deal, person/contact) are consistent. - Contribute to the existing dbt project (add or adjust models/tests) when AI features or Retool apps depend on it. Operationalization and handover - Document pipelines, services, and apps to a standard that allows handover to internal staff or future suppliers. - Add monitoring, alerting, and simple CI/CD where proportionate. Candidate Profile (Screening Criteria): Essential - 4-7+ years in software/data/platform engineering delivering production workloads. Strong Python (data processing, API integrations, automation, packaging). Demonstrable experience with AWS for data/app workloads (S3, Lambda, IAM, scheduling/eventing, Secrets Manager). Hands-on delivery of AI-/LLM-backed features or workflow - for example RAG, semantic search, text classification, or automated summarisation. Experience building internal tools in Retool or an equivalent low-code/internal-app environment. Solid SQL skills and ability to work within an existing transformation layer (dbt or similar). Ability to engage directly with business users, clarify requirements, and report progress. Desirable: Experience integrating with Slack and Salesforce (REST/Bulk APIs, object model, upserts). Familiarity with vector databases / search technologies and RAG-style architectures. Experience in venture capital / private equity / financial or data-forward professional services settings. Technical Environment Languages: Python, SQL Data / Transformations: DBT (existing) Cloud: AWS (S3, Lambda, IAM, EventBridge/Step Functions, Secrets Manager; Redshift/Athena as applicable) Internal Apps: Retool Business Systems: Salesforce, Slack AI / ML: LLM APIs, document processing, workflow agents, embedding/retrieval services, evaluation/monitoring utilities Years of Experience: around 5 How many years of experience would you consider overqualified? 8+ How many years of experience would you consider underqualified? Under 3