We are a growing AI-focused IT services company expanding into structured B2B outbound sales. To accelerate this growth, we’re hiring a Sales Development Representative (SDR) with hands-on GoHighLevel (GHL) expertise to set up and run our outbound engine. Key Responsibilities Build and maintain targeted prospect lists using Apollo, Cognism, LinkedIn Sales Navigator, etc. Identify and qualify leads matching our ICP (Ideal Customer Profile). Set up and manage outreach workflows inside GHL (email, SMS, WhatsApp, voicemail drops, LinkedIn tasks). Personalize and send outbound email/SMS campaigns with high deliverability standards. Monitor and optimize sequences for higher response and booking rates. Drive qualified prospects to book meetings through our GHL calendar system. Ensure reminders and follow-ups are in place to reduce no-shows. Track outreach KPIs: emails sent, response rates, meetings booked. Suggest improvements in ICP targeting, copywriting, and automation flows. Maintain pipeline hygiene inside GHL CRM. Skills and Qualifications 1–3 years experience in B2B sales, lead generation, or appointment setting. Hands-on experience with GoHighLevel (GHL) is a must. Familiarity with Apollo, Cognism, ZoomInfo, Lusha, or LinkedIn Sales Navigator. Strong understanding of cold email best practices, deliverability, and personalization. Good written English communication for outbound copywriting. Self-driven, detail-oriented, and metrics-focused. If you’re passionate about outbound sales and know how to get the most out of GHL and data tools, we’d love to hear from you.
We’re looking for a Generative AI Engineer who knows how to turn models into real-world systems—not just by fine-tuning, but by mastering prompting, template-driven workflows, and agentic AI architectures. You’ll build intelligent agents, design predictable prompt frameworks, and use tools like LangGraph to orchestrate complex, multi-step AI behaviors inside production-grade products. Responsibilities Prompting & Template Engineering • Design, optimize, and maintain prompt templates, reusable prompt frameworks, and evaluation pipelines. • Implement structured prompting patterns (chain-of-thought, RAG prompting, tool-calling prompts, guardrails, etc.). • Build automated systems for prompt testing, versioning, and performance tracking. Agentic AI & LangGraph Development • Build agent workflows using LangGraph (or similar frameworks) for stateful, multi-step reasoning. • Add and manage tool integrations (APIs, vector DBs, custom functions) into agent pipelines. • Ensure deterministic, safe, and traceable agent execution for real-world applications. Data & Model Interaction • Work with embeddings, vector DBs, retrieval pipelines, and dataset preprocessing. • Evaluate and refine model outputs through prompt adjustments rather than brute-force fine-tuning. • Collaborate on model selection, system design, and performance optimization. Deployment & Integration • Work with engineering teams to embed agentic AI into web apps, internal tools, and customer-facing solutions. • Support the creation of reusable AI modules, internal APIs, and automation assistants. Research & Continuous Improvement • Experiment with new agent frameworks, prompting techniques, and orchestration tools. • Stay current with frontier models, agent systems, and best practices. Requirements (1–2 Years Experience in AI and Overall experience of at least 2 years) • Strong understanding of prompt engineering, structured prompting, and model behavior. • Hands-on experience with LangGraph, LangChain, or agentic AI frameworks. • Good Python skills and familiarity with API integrations. • Practical experience with vector databases (Pinecone, Weaviate, Chroma, Supabase, Qdrant, etc.). • Ability to design RAG workflows, tool-calling prompts, and agent reasoning flows. • Familiarity with LLM APIs (OpenAI, Anthropic, Azure OpenAI, etc.). • Solid grasp of evaluation methods for LLM outputs and prompt performance. • Understanding of basic ML concepts (tokenization, embeddings, model inference, evaluation). Nice to Have • Experience building production-grade AI assistants or multi-agent systems. • Exposure to Autonomous Agents, LangGraph dynamic state machines, or tool-based agent routing. • Knowledge of lightweight fine-tuning (LoRA, QLoRA, adapters) and dataset generation. • Understanding of orchestration tools (n8n, make). • Experience with cloud platforms (Azure, AWS, GCP).