Senior AI/ML Engineer / Claude architect — Legal Tech FirmProfit AI is the operational backbone of the modern law firm. We automate law firm operations end to end, and we're looking for a top-tier AI/ML engineer to help us build the next major platform in legal tech. We need a true expert. Someone deeply proficient with Claude and modern LLM architecture who has shipped real products at a high level. You're fluent across the full stack with Node.js, React, Postgres, MongoDB etc... and you have hands-on experience building with LangChain, LangGraph, MCP, and AWS Bedrock. We're not looking for someone who's read about LLMs. We're looking for someone who has shipped agents, orchestration layers, and production AI systems that real users depend on every day. Our current team is 8 engineers, we have firms signed and live, and we're moving fast. This is a chance to come in early, and have your work in the hands of customers within weeks. Contract to start, with a long-term path for the right person. Reply with the most impressive AI product you've shipped.

About the Role: We are seeking a highly qualified Senior Machine Learning and Natural Language Processing Engineer with deep expertise in sentence parsing, contextual understanding, categorization, and language extraction to support and advance Sybal’s Proof of Governance® (PoG™) platform. This role blends advanced NLP engineering, full-stack development, and enterprise-grade deployment. You will design custom NLP models, build scalable AI-driven services, and deploy production-ready applications that transform raw policy and technical language into structured governance intelligence. You must be a senior-level full-stack engineer proficient in Python, Django, JavaScript, HTML, and CSS, with the ability to dockerize and deploy applications into production environments. Experience commercializing enterprise AI applications is required. You should also be familiar with using agentic AI tools in a development context—for debugging, workflow acceleration, rapid prototyping, and improving engineering efficiency. ________________________________________ Key Responsibilities: NLP & Machine Learning Engineering: • Build advanced NLP models for sentence parsing, context detection, semantic analysis, entity extraction, and policy language interpretation. • Develop hybrid ML + rule-based systems that support governance modeling and policy decomposition. • Create pipelines for text ingestion, annotation, categorization, and structured language extraction. • Design evaluation frameworks for accuracy, drift, reliability, and linguistic precision. • Research and implement non-LLM NLP methods relevant to governance and policy analysis. Full-Stack Engineering: • Develop production-ready applications using Python (spaCy, NLTK, TensorFlow, or PyTorch to build and optimize NLP models), Django, JavaScript, HTML, CSS, and modern tooling. • Further develop NLP models for PoG™ Feature enhancements. • Develop and maintain secure, scalable REST APIs and backend services. • Integrate ML components seamlessly into PoG™’s architecture. Production Deployment & DevOps: • Dockerize machine learning pipelines and full-stack applications for uniform deployment. • Deploy and manage services in cloud production environments (AWS, GCP, or Azure). • Set up CI/CD pipelines, monitoring, observability, and scalable containerized processes. • Ensure production performance, uptime, and system reliability. AI Automation for Engineering Efficiency: • Use agentic AI tools to assist with debugging, test generation, workload orchestration, and internal development workflows. • Integrate AI-assisted coding tools responsibly into engineering processes. Contribute to the Proof of Governance® Platform: • Build NLP and ML components that strengthen PoG™’s ability to: • Map policy language into structured governance data • Detect enforceability gaps • Identify policy dependencies and contextual interactions • Deliver measurable, enforceable governance intelligence • Collaborate with PoG™ architects to extend platform intelligence across governance domains. ________________________________________ Qualifications: Required Skills & Experience: • 6–10+ years of software engineering experience with specialization in ML and NLP. • Mastery of sentence parsing, syntax/semantic analysis, dependency modeling, and contextual extraction. • Proven experience commercializing enterprise AI or ML-driven applications. • Proficiency in: o Python o Django o JavaScript o HTML / CSS • Demonstrated ability to dockerize applications and deploy them into production. • Strong understanding of ML architecture, data modeling, distributed systems, and backend engineering. • Experience using agentic AI tools for engineering workflows (debugging, code analysis, test generation). • Strong cloud engineering experience (AWS, GCP, Azure). Preferred Qualifications: • Background in computational linguistics or structured policy analysis. • Experience with ontologies, taxonomies, or governance modeling. • Prior work in regulated, audit-heavy, or mission-critical environments. • Contributions to high-scale enterprise software platforms. ________________________________________ Who You Are: • You excel in both advanced NLP engineering and full-stack software development. • You can design systems end-to-end—from custom algorithms through front-end integration to production deployment. • You understand how to use AI to accelerate development processes. • You are driven by building systems that transform governance from assumption to measurable, enforceable proof. • You are excited to contribute to the continuous evolution of PoG™

We are hiring an AI Engineer with strong hands-on experience building and shipping real AI products. Requirement: If you don't have a GitHub profile to share, this role is not a fit. What we’re looking for: • Strong experience in AI/ML engineering • Ability to build, test, and deploy production-ready AI systems • Practical experience working on real-world AI projects To apply: Please share your portfolio, past AI projects, and relevant work samples. Applicants without portfolio will be ignored.

We're hiring a senior AI developer to build and deploy AI solutions for a fintech/credit-union platform. The work spans autonomous banking agents, fraud detection, credit scoring, and bill-pay/invoice automation — at the intersection of LLMs, cloud infrastructure, and financial-domain expertise, with security and compliance built in from the start. This is a long-term, ongoing engagement. What you'll do: AI agents & orchestration - Design, build, and deploy multi-agent systems using Amazon Bedrock Agents, LangChain, and related frameworks - Architect agentic workflows for core banking use cases: credit scoring, fraud detection, bill-pay automation, invoice management - Define agent personas, memory strategies, tool-use patterns, and escalation paths for production banking agents LLM engineering - Fine-tune, prompt-engineer, and evaluate LLMs for financial-domain tasks - Build RAG pipelines over credit-union knowledge bases, policy docs, and member data - Implement guardrails, content filtering, and compliance checks for safe, regulated outputs - Monitor performance, hallucination rates, and latency against SLAs Cloud infrastructure (AWS & Azure) - Architect and manage AI/ML workloads on AWS (Bedrock, SageMaker, Lambda, S3, IAM, VPC) and Azure (OpenAI Service, Azure ML, AKS) - Design secure, cost-optimized environments compliant with NCUA, PCI-DSS, and SOC 2 - Implement infrastructure-as-code with Terraform or AWS CDK DevOps & MLOps - Build and maintain CI/CD pipelines (GitHub Actions, Jenkins, CodePipeline, Azure DevOps) - Containerize services with Docker, orchestrate with Kubernetes (EKS/AKS) - Apply MLOps best practices: model versioning, A/B testing, canary deployments, automated rollback - Stand up observability with logging, tracing, and alerting Python development - Write clean, well-tested Python for AI pipelines, REST APIs, and data workflows - Build FastAPI/Flask microservices exposing agent capabilities to frontend and core banking systems - Integrate with financial data sources, core banking APIs, and third-party fintech services Banking applications - Build credit-scoring models using alternative data and explainable AI (XAI) - Develop real-time fraud detection with behavioral analytics, anomaly detection, and auto-decisioning - Create conversational agents for bill pay, account management, and member self-service - Automate invoice workflows: extraction, classification, approval routing, reconciliation - Partner with compliance/risk to keep AI decisions auditable, fair, and regulatory-compliant What you should have: - 5+ years software engineering; 3+ years in AI/ML or LLM engineering - 2+ years building AI for banking, credit unions, or financial services - Hands-on experience with Amazon Bedrock, LangChain, Python, AWS, and infrastructure-as-code - Working knowledge of NCUA, PCI-DSS, SOC 2, GLBA, and Fair Lending requirements - Bachelor's or Master's in Computer Science, Software Engineering, Data Science, or related field Nice to have: - AWS or Azure AI/ML certifications - Open-source LLM experience (Llama, Mistral, Phi) and self-hosted inference (vLLM, Ollama) - Vector databases (Pinecone, OpenSearch, pgvector) - Graph-based fraud networks and graph ML - AI governance / responsible AI framework experience - Prior work at a credit union, community bank, or fintech lending platform To apply, please share: - Your resume highlighting AI and banking project experience - A brief note on your most impactful AI agent or LLM project in a financial-services context - Links to GitHub, portfolio, or published papers (optional but encouraged)

Eligibility: This role is open to U.S. citizens only due to client security and compliance requirements. Please apply through this posting only — do not contact Data-Sleek directly regarding this position. Applications received outside this channel will not be considered and reported to Upwork. Data-Sleek is looking for a Senior AI Solutions Engineer to lead our on-premise and government-cloud AI deployments. You will design, build, and deploy AI-powered data pipelines for clients who cannot use commercial cloud due to ITAR, CMMC, or other data residency constraints, beginning with a client in the aerospace and defense sector. Beyond this first engagement, you will become Data-Sleek's go-to engineer for AI deployments across defense and aerospace clients, building the practice rather than just executing a single project. About Data-Sleek Founded in 2020, Data‑Sleek® is a U.S.-based AI and data consulting firm that helps mid-market companies build the data foundation that AI actually runs on. We own the full path — data strategy, architecture, integration, warehousing, and AI implementation — so organizations can adopt AI with confidence, stay compliant, and scale, without first hiring an internal data team. Our distributed U.S. team (San Francisco, Los Angeles, Irvine, Dallas, Chicago, and New York) partners with clients across healthcare, finance, insurance, logistics, and technology, modernizing data platforms with best-in-class tools like Snowflake, dbt, Fivetran, Tableau, and AWS. Trusted by Fortune 500 institutions and growing companies alike, Data‑Sleek turns complex data into measurable outcomes — faster insight, lower cost, and AI projects that deliver. About the Role You will own the technical delivery of AI-powered data pipelines in restricted environments where commercial cloud is not an option. The immediate engagement centers on a Product Lifecycle Management (PLM) data migration: building a pipeline that connects to a client's SharePoint on a restricted Microsoft 365 government tenant, reads engineering documents, classifies and summarizes them, detects duplicates, and rates naming-convention compliance to produce a migration-readiness report. You will start on-premise, then help the client evaluate and move to government cloud for production. Key Responsibilities AI Pipeline Development Build AI pipelines that connect to a client's SharePoint on a government cloud tenant, read engineering documents, classify them by type, generate summaries, detect duplicates, and rate naming-convention compliance in support of PLM data migration. Catalog large document repositories and produce migration-readiness reports and Excel catalogs that give clients a clear, measurable picture of their data. Engineer document-parsing workflows across DOCX, PDF, and XLSX formats, including embedding generation and database operations. On-Premise & Government Cloud Deployment Deploy on-premise first — a Mac Mini running Gemma via Ollama — standing up, serving, and tuning local inference infrastructure. Evaluate and migrate to production on Azure OpenAI (Azure Government) or AWS Bedrock (GovCloud) when the client is ready to scale. Keep deployments compliant within ITAR-sensitive, restricted-network boundaries throughout. Architecture & Cost Advisory Produce cost models and architecture recommendations that help client IT teams make informed platform decisions based on measured data, not vendor pitches. Compare deployment options — local, Azure Government, and AWS GovCloud — on cost, performance, and compliance, and explain the trade-offs clearly. Practice Building & Delivery Serve as Data-Sleek's go-to engineer for AI deployments across defense and aerospace clients. Build a reusable capability — a repeatable AI-solutions practice — rather than executing a single one-off project. What You Bring Required U.S. Citizen: U.S. citizenship is required and non-negotiable due to ITAR and client security and compliance requirements. Production LLM deployment: You have stood up inference infrastructure — not just called an API. You've handled model loading, memory constraints, failure modes, and throughput tuning in a real deployment. Local inference: Ollama, vLLM, llama.cpp, LM Studio, or TGI. You've served open-source models (Gemma, Llama, Mistral) on local hardware. Cloud AI platforms: Azure OpenAI or AWS Bedrock — at least one. Service configuration, model access, authentication, and token-based pricing. Python: Pipeline engineering — document parsing (DOCX, PDF, XLSX), API integrations, embedding generation, and database operations (SQLite, Postgres). Experience: 5+ years post-degree in software engineering, data engineering, or ML engineering. Strong Preferences Microsoft ecosystem: Entra ID, Microsoft Graph API, and SharePoint REST API at the API level. GCC High experience is a bonus. MCP (Model Context Protocol): Experience building or consuming MCP servers — a significant plus for a fast-evolving protocol. Workflow orchestration: n8n, Temporal, Airflow, or similar. The pipeline is orchestrated, not scripted. Government cloud awareness: Understanding of what FedRAMP High, IL4/IL5, and ITAR mean for cloud architecture decisions. Embeddings & vector similarity: sentence-transformers, pgvector, Qdrant, or FAISS for duplicate detection. 
Bonus (valued if present) Aerospace or defense experience: Familiarity with ECOs, BOMs, and AS9100 saves ramp time. Apple Silicon optimization: MLX, Metal acceleration, and Ollama tuning on M-series chips. Agentic frameworks: Bedrock AgentCore or Azure AI Foundry — the future direction involves agentic AI workflows on government cloud. What This Role Is Not Model training or fine-tuning. This is deployment engineering, not research. Data science or statistical modeling. The AI here is document understanding and classification, not predictive analytics. Frontend development. The deliverable is an Excel catalog and a report, not a web app. Sales or client acquisition. Data-Sleek's leadership manages the client relationship; you focus on delivery. Engagement & Compensation Remote, US-based. Occasional on-site travel to client facilities for hardware deployment and workshops may be needed. An average of 2–3 trips for the first engagement may be possible. Compensation. $40-$55/hour Why Join Data-Sleek? At Data-Sleek, you'll lead AI deployments in environments most engineers never touch — government cloud and on-premise systems where commercial tools simply aren't an option. Your work will directly shape how defense and aerospace clients adopt AI, and you'll build a reusable capability the company grows around. We focus on doing the right thing architecturally rather than selling the most expensive option, and we give our engineers the autonomy to deliver real solutions for real constraints. How to Apply If you've shipped real LLM deployments with real constraints, we'd like to hear from you. Please submit: Your resume A brief note describing one LLM deployment you've shipped — what model, what infrastructure, what data source, and what went wrong. Data-Sleek® is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all contractors.

Work description We are looking for an experienced Java / Spring Boot developer who can help design and build backend applications with AI integration. The ideal candidate should have strong experience in Java-based microservices, REST APIs, cloud deployment, and integrating AI/LLM services into business applications. The work may include building APIs, connecting with AI models, integrating OpenAI/Azure OpenAI/LLM services, creating AI agents or automation workflows, and improving existing backend systems. Responsibilities: Design and develop scalable backend services using Java and Spring Boot. Integrate AI/LLM APIs such as OpenAI, Azure OpenAI, LangChain4j, Semantic Kernel, or similar tools. Build RESTful APIs for AI-powered features. Work with databases, authentication, external APIs, and cloud services. Optimize performance, security, and reliability of backend services. Write clean, maintainable, and well-tested code. Troubleshoot production issues and improve existing applications. Required Skills: Strong experience with Java 8/11/17+ and Spring Boot. Experience with REST APIs, microservices, and JSON. Hands-on experience integrating AI/LLM APIs. Knowledge of prompt engineering, embeddings, RAG, vector databases, or AI agents is a plus. Experience with SQL/NoSQL databases. Familiarity with AWS, Azure, or GCP. Good communication and ability to work independently. Preferred Skills: Azure OpenAI or OpenAI API integration. LangChain4j, Spring AI, or Semantic Kernel experience. Vector DB experience such as Pinecone, Chroma, Weaviate, FAISS, or Azure AI Search. Docker, Kubernetes, CI/CD knowledge. Healthcare, finance, or enterprise application experience is a plus. Project Goal: We need a Java expert who can help us build AI-powered backend features and integrate them into our existing application in a scalable and secure way. To Apply: Please share examples of Java/Spring Boot projects you have worked on, especially any projects involving AI, LLMs, automation, or API integrations.

We are hiring a senior software engineer to build and maintain high-performance libraries and the services that depend on them. This is a hands-on, individual-contributor role for an engineer who cares deeply about correctness, clean APIs, and robust, well-tested code. You will own performance-critical code end to end, from API design through testing, benchmarking, and packaging. This is not a research role. The work is applied: integrating well-understood primitives correctly, exposing them through clean interfaces, and making sure everything is fast, reproducible, and thoroughly tested. What you will do Design and maintain idiomatic, modern Rust libraries with trait-based, minimal public APIs. Build, package, and test native bindings that expose a compiled Rust core to other languages (for example, Python via FFI). Integrate standard cryptographic primitives correctly and reason carefully about their use. Write and reason about ML test workflows, including how data transformations affect model behavior. Maintain a rigorous testing and benchmarking discipline across the codebase. Must-have skills Senior-level Rust. Idiomatic, modern Rust; trait-based API design; async programming; strong command of the toolchain (clippy, rustfmt, cargo workspaces). Applied cryptography fundamentals. Comfortable working with standard cryptographic primitives (authenticated encryption, hashing and keyed hashes, key derivation) and reasoning carefully about their correct use. This is library usage and integration, not novel algorithm research. Cross-language bindings. Experience building, packaging, and testing native bindings that expose a compiled core to another language (for example, Python via FFI). Machine-learning literacy. Enough to write and reason about ML test workflows and to understand how data transformations affect model behavior. Strong testing discipline. Unit, integration, and property-based testing; deterministic fixtures; and benchmarking of performance-critical paths. Strong-plus skills Python engineering, especially data pipelines and packaging (columnar formats, object storage, cloud SDKs). TypeScript library and SDK development with a modern, schema-first toolchain. Cloud and ML-platform packaging, especially AWS. Nice-to-have skills Smart-contract and blockchain development (for example, Solana/Anchor, or Daml). Technical-documentation and API-documentation tooling. Engineering practices we value A bias toward robust, production-ready solutions over quick workarounds. Reproducible, deterministic builds and disciplined dependency management. Small, well-scoped changes with clear commit history and documentation kept in sync with code. A preference for one clean, canonical way to do things and a minimal public API surface. Engagement details Contract type: [Hourly or fixed-price] Estimated hours: [e.g., 20 to 40 hours per week] Duration: [e.g., 3+ months, ongoing] Experience level: Expert Time zone overlap: [e.g., several hours of overlap with US Pacific]

Seeking an experienced AI Solutions Architect to provide consultation and design an enterprise-grade Agentic AI platform capable of automating business workflows, retrieving knowledge from enterprise data sources, and integrating with existing business systems. This engagement focuses on solution architecture, technical design, technology selection, implementation planning, and best practices rather than full-scale application development. Scope of Consultation The consultant will: Assess current business processes and identify high-value AI automation opportunities. Design an enterprise Agentic AI architecture aligned with business and technical requirements. Define multi-agent workflows, agent responsibilities, and orchestration strategies. Design a scalable Retrieval-Augmented Generation (RAG) architecture for enterprise knowledge retrieval. Recommend the appropriate Large Language Models (OpenAI, Claude, Gemini, AWS Bedrock, Azure OpenAI, etc.) based on cost, performance, and use cases. Recommend vector database technologies and semantic search architecture. Design secure integrations with enterprise applications, APIs, and internal knowledge repositories. Define prompt engineering strategies, AI guardrails, evaluation methodology, and governance practices. Recommend cloud architecture and deployment strategies for AWS, Azure, or Google Cloud Platform. Provide guidance on LLMOps, monitoring, observability, security, model lifecycle management, and scalability. Develop an implementation roadmap, including phases, estimated effort, risks, and technical recommendations. Required Expertise Enterprise AI Solution Architecture Agentic AI Multi-Agent Systems Retrieval-Augmented Generation (RAG) Large Language Models (LLMs) LangChain LangGraph Prompt Engineering OpenAI API Anthropic Claude Google Gemini AWS Bedrock Python Vector Databases Enterprise System Integration AWS Microsoft Azure Google Cloud Platform AI Governance LLMOps MLOps Workflow Automation Deliverables Enterprise Agentic AI solution architecture document Multi-agent workflow design and orchestration diagrams RAG architecture and knowledge management design Vector database recommendation and data flow architecture Cloud deployment architecture Integration strategy for enterprise systems AI governance, security, and LLMOps recommendations Implementation roadmap with milestones and estimated effort Architecture review presentation and knowledge transfer session Project Outcome Delivered a comprehensive enterprise AI architecture and implementation strategy that provides a scalable foundation for deploying Agentic AI solutions. The consultation enabled stakeholders to make informed technology decisions, reduce implementation risks, accelerate development, and establish best practices for governance, security, and long-term operational success.

We are hiring an AI Engineer for a remote opportunity with our Airlines project. The ideal candidate should have hands-on experience building GenAI solutions, including RAG pipelines, vector embeddings, prompt engineering, MCP server development, and integrating multiple LLM providers. Experience working with AWS Neptune (Graph DB), OpenSearch (Vector Store), Redis, REST APIs, and SSE-based streaming services is required. Exposure to LangChain, MCPSharp, or ModelContextProtocol.SDK is a plus. If interested, please share your updated resume along with your total years of experience, years of GenAI experience, RAG experience, MCP/Agentic AI experience, current location, work authorization, and availability to start.