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.

I’m looking for an AI Engineer to help build an automated red-teaming product based on open-source models. This is a short-term, hands-on project for around 2 months, with an expected commitment of about 20 hours per week. The goal is to build a specialized red-teaming engine that can generate adversarial prompts across different risk domains, severity levels, and attack strategies — then automatically run those prompts against target AI models to identify bad cases, failure patterns, and safety gaps. 🔍 What you’ll work on Build red-teaming systems on top of open-source LLMs, including fine-tuning, prompt optimization, evaluation pipelines, and model orchestration. Design automated prompt generation workflows across risk domains such as self-harm, hate, violence, sexual safety, misinformation, fraud, cyber, and other high-risk areas. Generate prompts across different harm levels, from benign edge cases to policy-borderline and clearly unsafe scenarios, while maintaining structured taxonomies and evaluation criteria. Run automated tests against target models such as Gemma, Llama, Qwen, or other open-source / closed-source models to surface jailbreak patterns, over-refusal, under-refusal, and policy inconsistencies. Build feedback loops that turn model failures into stronger red-team prompts, improved eval sets, remediation recommendations, and continuous safety testing. 🧠 What I’m looking for Hands-on experience with open-source LLMs, fine-tuning, LoRA / QLoRA, RAG, model evaluation, and LLM inference pipelines. Familiarity with AI safety, red teaming, adversarial prompting, jailbreaks, safety evals, or trust & safety systems. Ability to build end-to-end systems, including data pipelines, model serving, eval harnesses, scoring, dashboards, and automation workflows. Bonus if you’ve worked on model safety, content moderation, policy evaluation, agentic testing, or automated eval infrastructure. ⏳ Project setup Duration: around 2 months Time commitment: about 20 hours per week Format: flexible / remote-friendly Stage: early-stage build, from 0 to 1 🚀 Why this is interesting This is not about manually writing red-team prompts one by one. The goal is to build a scalable system that can continuously generate, test, categorize, and learn from model failures — helping teams understand where AI models break, why they break, and how to improve them. If you enjoy working with open-source models, AI safety, red teaming, and fast 0-to-1 product building, I’d love to chat. Feel free to DM me if this sounds like you, or if you know someone who might be a good fit.

We are looking for a hands-on Forward Deployed AI Engineer to help build practical AI systems This is not a pure backend role and not a strategy-only consulting role. You will work close to end users, understand how their workflows actually operate, and then build AI-enabled tools that solve specific business problems. The ideal person is a strong software engineer who is comfortable with ambiguity, can communicate clearly with non-technical stakeholders, and can take an AI prototype from idea to something reliable and usable. What you will do - Learn the business workflows, systems, data, and constraints. - Build AI applications using Claude or similar large language models. - Use the right mix of prompting, retrieval, tool use, agents, and workflow automation. - Own delivery from scoping through prototype, testing, hardening, and handoff. - Create evaluations to determine whether the system is accurate, reliable, and safe enough to use. - Translate between domain experts and technical implementation. - Work carefully with sensitive or regulated data. - Document what you build so it can be maintained and reused. What we are looking for - Strong Python engineering skills. - Hands-on experience building with LLMs, preferably Claude or the Anthropic API. - Experience with RAG, structured prompting, tool use, evaluation, or agentic workflows. - Ability to operate independently in a messy, ambiguous environment. - Strong communication skills with both technical and non-technical stakeholders. - Track record of shipping working software, not just demos. - Comfort working with real-world data, integrations, and imperfect requirements. Helpful but not required - Prior forward deployed engineering, solutions engineering, or technical consulting experience. - Experience building AI tools for enterprise customers. - Experience in regulated or sensitive-data environments. - Familiarity with validation, auditability, traceability, or compliance-oriented workflows.

Senior Software Engineer (AI-Focused, Contract – US) Position Summary W Energy is seeking a Senior Software Engineer (Contract) to help drive the integration of AI capabilities into our core platform. This role is focused on building AI-powered product features, not just experimenting with models—embedding intelligence directly into workflows across our upstream and midstream solutions. You’ll design and implement AI-driven functionality that improves automation and user experience. This includes leveraging LLMs, machine learning models, and modern AI tooling within a production SaaS environment. This is a hands-on role for someone who can move quickly, make pragmatic decisions, and bring AI concepts into real, scalable product features. Responsibilities • Design and implement AI-powered features within the platform (e.g., automation, recommendations, copilots) • Integrate LLMs and/or ML models into existing services and workflows • Evaluate, select, and optimize AI tools, APIs, and frameworks for production use • Collaborate with Product to translate business problems into AI-driven solutions • Build and maintain scalable backend services to support AI functionality • Profile, test, and optimize performance of AI-integrated systems • Ensure reliability, security, and cost-efficiency of AI components in production • Contribute to architecture decisions around AI integration and system design • Partner with engineering teams to embed AI into existing applications without degrading stability Requirements • 5+ years of experience as a software engineer in a SaaS or cloud-based environment • Strong backend engineering experience (RoR and/or Golang preferred) • Experience integrating APIs and working within distributed systems • Hands-on experience with AI/ML tools (e.g., OpenAI, Anthropic, Hugging Face, or similar) • Experience building or integrating AI-powered features into applications (not just experimentation) • Strong understanding of data flow, system design, and performance optimization • Experience with relational databases (SQL Server or similar) • Familiarity with microservices architecture, Kubernetes, and CI/CD pipelines • Experience deploying applications in Azure or similar cloud environments • Strong problem-solving skills with ability to work in ambiguous, fast-moving environments • Builder mindset—someone who can take an idea and turn it into a working feature quickly • Pragmatic approach to AI (focus on value, not hype) • Ability to work independently in a contract environment while collaborating closely with internal teams • Strong communication skills and ability to explain AI concepts to non-technical stakeholders Preferred • Experience with prompt engineering, embeddings, or retrieval-augmented generation (RAG) • Exposure to model evaluation, fine-tuning, or AI performance monitoring • Experience with event-driven architectures or real-time data processing • Background in energy, fintech, or other complex data-driven industries

Overview We run an AI voice assistant for self-storage operators. We have an internal, AI-assisted workflow for triaging call feedback — investigating what happened on a call, diagnosing the root cause in our codebase, and drafting fixes. We’re looking for someone technical to run that AI-assisted workflow day to day and help us make it better. You’ll be driving AI coding agents, reading real code to understand behavior, and improving the process and tooling itself. What you’ll do Use our AI agent tooling to work through a queue of customer feedback on AI voice calls. Read our TypeScript/Node codebase (voice-agent prompt assembly, workflow/“SOP” engine, tool implementations) to diagnose why the agent behaved a certain way — not just guess. Draft fixes: workflow-instruction edits, knowledge-base entries, or code changes via pull request with a clear verification plan. Improve the triage process itself — refine the AI agent prompts/skills, conventions, and the internal MCP tooling that powers it. Write clear, customer-facing summaries of what changed for our team to review and approve. You’re a great fit if you Read and reason about code confidently — ideally TypeScript/Node; React a plus. Have hands-on experience driving AI coding agents (Claude Code, Cursor, or similar) and understand how LLM prompts/tools/agents fit together. Think in cause-and-effect: “the agent did X because line Y / instruction Z.” Write precisely and concisely for both technical and non-technical audiences. Are process-minded — you spot the repetitive thing and turn it into a better workflow. Bonus: prompt engineering, LLM tool/agent development, or voice/conversational AI experience. How we work We’ll start with a paid trial on a small batch, then scale steady ongoing volume. To apply: Tell us about a time you used an AI coding agent to diagnose or fix something non-trivial in a codebase you didn’t write — what you did, and how you verified it worked. A link to relevant work is a plus.

AI Engineer (RAG & Agentic Workflows). *LLM RESPONSES AUTOMATICALLY AVOIDED* We have already launched a production generative AI product that utilizes a custom Retrieval-Augmented Generation (RAG) architecture. We are now expanding the platform to include CRM intelligence, workflow automation, and agentic AI capabilities. This is **not** a prompt engineering role. Seeking an engineer with deep experience building and deploying production AI systems that combine LLMs with multiple structured and unstructured data sources. You should be comfortable walking into an existing, complex codebase, understanding the current architecture, and improving it. Existing AI Architecture Our current AI architecture consists of: * OpenAI embeddings * Embeddings stored in MongoDB * MongoDB Atlas Vector Search for retrieval * Retrieval from both structured SQL data and unstructured document collections * Existing tool/function-calling architecture **Please do not apply if you have not previously built or maintained production RAG systems using embeddings and vector search.** Experience specifically with **OpenAI embeddings and MongoDB Atlas Vector Search** is highly preferred. CRM Intelligence Layer We are currently building a CRM platform and need the AI to reason over CRM records, including the other records are RAG currently retrieves. You will be responsible for designing and implementing the AI integration layer that enables the LLM to intelligently retrieve and reason over CRM data. This work includes: * Designing AI tools/functions that expose CRM data to the LLM. * Implementing backend tool handlers that retrieve CRM records. * Defining tool schemas and instructions so the AI knows when and how to retrieve CRM information. * Building secure retrieval mechanisms that enforce strict user and organization-level access controls. * Transforming raw CRM records into structured, AI-ready context. The AI will need to reason across: * CRM contacts and organizations * client profiles * Deals and opportunities * Projects * Tasks and reminders * Notes * Email history * SMS and WhatsApp communications * Call transcripts * Meeting summaries * Documents and contracts * Workflow history Agentic AI & Workflow Automation * Build proactive AI agents that generate alerts, recommendations, follow-ups, reports, and suggested next actions. * Design systems capable of reasoning across both structured and unstructured data sources. * Architect and implement multi-step and multi-agent workflows. * Develop workflow intelligence that assists users in completing real-world business tasks. Required Experience * Demonstrated experience building and deploying production AI systems used by real customers. * Experience working with embeddings, vector databases, and retrieval pipelines. * Experience implementing LLM tool/function-calling architectures. * Experience integrating AI systems with business systems such as CRMs, ERPs, or other operational databases. * Experience combining structured and unstructured data within AI applications. * Strong backend engineering and systems architecture experience. * Demonstrated ability to quickly understand and improve existing codebases. * Ability to independently own and deliver complex technical initiatives. Strongly Preferred * Experience with OpenAI embeddings. * Experience with MongoDB Atlas Vector Search. * Experience building agentic AI systems and workflow automation. * Experience designing long-term memory architectures. * Experience building multi-tenant SaaS applications with strict authorization requirements. * Experience implementing evaluation and monitoring pipelines for production AI systems. What We Value * High accountability and ownership. * Strong communication skills. * Product thinking and user empathy. * Ability to understand user workflows before writing code. * Pragmatism and sound engineering judgment. PLEASE DO NOT WASTE OUR TIME IF YOU NOT MEET THE REQUIREMENTS 

Seeking a LLM prompt engineer and solutions architect to develop AI medical note writing templates. The role involves creating structured templates for clinical documentation. Patient encounters will be turned into precise medical notes with good detail and reproducible note sections based on previous patient encounters. Also complete testing to ensure notes are compatible with my medical record system. The ideal candidate will have experience in medical documentation and AI solutions architecture.

Job Title - AI Native Developer Duration - 90 Days Initial Engagement (20 Hours/week) Work Mode- Onsite Work Location- Plano, Tx Position Summary: We are hiring a Senior AI Native Developer to join an active client engagement in the restaurant technology space. This is a hands-on, on-site role with a dual mandate: driving development execution as a Senior Engineer and acting as a client-embedded technical liaison — a Product Owner / Technical Lead hybrid — working alongside the client's CTO, stakeholders, and ISHIR's India-based development team. This role suits someone who thrives in ambiguity, translates business requirements into shipped code, and is equally comfortable presenting to executive stakeholders and writing production-grade AI features in the same week. WHAT YOU'LL DO Client Delivery & On-site Presence Serve as the primary on-site technical SPOC for the client engagement — attend stand-ups, sprint reviews, and stakeholder sessions Represent ISHIR's delivery team with clarity and professionalism; communicate progress, risks, and decisions to CTO and CFO-level stakeholders Facilitate product discovery, requirements refinement, and architecture review sessions on-site Own the client relationship day-to-day, proactively flagging issues before they escalate AI-Native Development Design and build AI-powered product features using LLMs, RAG pipelines, and agentic architectures Integrate AI capabilities — OpenAI, Anthropic Claude, Azure OpenAI — into production web and API systems Write clean, testable, production-grade code across the full stack (frontend through backend through data layer) Own feature delivery end-to-end: from acceptance criteria through deployment and post-go-live monitoring POD Coordination Collaborate daily with ISHIR's India-based developers and QA across time zones via Azure DevOps, Slack, and video standups Translate client decisions and feedback into clear development tasks and backlog items for the offshore team Review pull requests and maintain code quality standards across a distributed team Flag scope changes, technical blockers, and delivery risks to the Program Manager proactively WHAT WE'RE LOOKING FOR Must-Have 8+ years of hands-on software engineering experience, with at least 2 years building AI/ML or LLM-integrated products Proven ability to work directly with clients — comfortable in executive meetings and boardroom-level presentations Strong full-stack background: Python or Node.js backend; React or equivalent frontend Hands-on experience with LLM APIs (OpenAI, Anthropic, Azure OpenAI) — prompt engineering, RAG, tool/function calling Solid agile delivery experience: sprint planning, backlog grooming, coordinating distributed engineering teams US-based with ability to travel on-site to client locations (primarily Dallas, TX area) Exceptional written and verbal communication — you write as clearly as you code Strong Advantages Prior experience in a client-facing technical lead, senior PO, or solutions architect capacity at a services or consulting firm Familiarity with restaurant technology, hospitality SaaS, or B2B platform products Experience with Azure DevOps, CI/CD pipelines, and cloud-native deployments (Azure preferred) Hands-on with agentic AI frameworks — LangChain, LangGraph, AutoGen, or similar Anthropic Claude Certified Architect (CCA-F) or equivalent AI platform certification ABOUT ISHIR ISHIR is a digital innovation and enterprise AI services provider. We work with startups and enterprises to shape the future through accelerated innovation, deep technical expertise, access to global digital talent and a passion for complex problem-solving. With our help, our clients overcome their most difficult digital challenges leveraging AI. We are not just consultants, we are partners in our clients’ success, assisting them with re(gaining) competitive edge by identifying opportunities for differentiation, industry disruption, scalable innovation, and go-to-market strategies that deliver successful outcomes. At ISHIR, we help bold businesses accelerate innovation through Talent, Speed-to-Market, and AI. We help make an impact by solving real problems using innovation, improved customer experiences and the right technologies. As an ISHIR employee, you will get the advanced training you need to be successful, and the opportunity to apply it. You must be passionate about technology, crave responsibility, and be eager to apply your knowledge to real business solutions for our startup and enterprise customers. These are the qualities of a person destined for success at ISHIR. ISHIR attracts a special type of individual—someone who is proactive, thrives on challenges, feeds off success, and looks at moving targets not as obstacles but as opportunities. ISHIR is an exciting place to work. It is imbued with an entrepreneurial spirit and promotes self-reliance, open communication, and collaboration

We are seeking a skilled GenAI engineer to work with our client in a remote or Chicago-based capacity. The ideal candidate will have experience in developing and implementing AI solutions, with a strong understanding of machine learning and data analysis. Responsibilities include designing AI models, integrating AI into existing systems, and collaborating with cross-functional teams to enhance AI capabilities. If you have a passion for AI and a proven track record in delivering innovative solutions, we would love to hear from you.

Building an AI-powered trading intelligence platform backed by one of the biggest stock-trading YouTubers in the space (7-figure audience, direct distribution to our exact target users from day one). We're not looking for product-market fit — we have a warm audience waiting. We're in the final push to V1 launch and shipping fast. What we've already built An AI Strategy Builder: traders describe a strategy in plain English, our LangGraph agent pipeline (Claude) turns it into code, and a NautilusTrader engine backtests it against years of tick-accurate market data A real data moat: TimescaleDB with 10 years of futures/equities data Live market intelligence: screeners, regime classification, probability models What you'll build You'd own big, meaty features end to end - not tickets, not maintenance: The strategy optimization engine: run 60+ market signals as filters over backtest results, rank by statistical impact, present improvements to users (this is our flagship differentiator) Statistical validation: walk-forward testing, overfitting protection Market calculators: VWAP, volume profile, pre-market levels A daily AI trading playbook delivered via text/Discord Real-time pipeline health monitoring Our stack Python/FastAPI · LangGraph + Claude (Anthropic API) · NautilusTrader · TimescaleDB/Postgres · React/TypeScript/Vite · NestJS · WebSockets · AWS Who we need Someone cracked. Specifically: 5+ years shipping production Python backends (FastAPI/Django/Flask) — you write code that survives contact with real users Real LLM engineering experience — agent pipelines, structured outputs, prompt-driven codegen, LangGraph/LangChain (or you've built equivalents from scratch) Strong SQL and data chops — you're comfortable with time-series data at scale Full-stack ability — you can carry a feature from Postgres to React without waiting on anyone Trading/quant/fintech domain experience is a big plus — you know what Sharpe, drawdown, and walk-forward mean without Googling You ship daily, communicate crisply in Slack, and don't need a PM to translate ambiguity into work Why this gig Direct line to founders, zero bureaucracy — your code hits production the week you write it Guaranteed distribution at launch via our backer's audience — your work gets used by thousands of real traders immediately Long-term engagement for the right person, with room to grow into a lead role