Build intelligent multi-step workflows using n8n, Zapier, or similar tools Develop and maintain Al agents with tools like LangChain, Llamalndex, or custom logic Connect LLMS (OpenAI, Claude) with external tools to create task-specific agents Automate content pipelines: blog image video social distribution Build and deploy lead generation, CRM enrichment, and cold outreach workflows Integrate APIs (Google Workspace, Notion, CapCut, Canva, ElevenLabs, etc.) Design modular automation logic with fallbacks, error handling, and monitoring Document workflows for clarity, reuse, and scalability Collaborate with technical and non-technical team members to ship solutions Required Skills Prompt chaining and memory with LangChain, LlamaIndex, RAG Using OpenAI, Claude, or custom LLM APIs in dynamic logic Tool-calling agents that interact with external APIs or functions Familiarity with vector databases (Pinecone, Chroma) for retrieval tasks Advanced workflow design in n8n, Zapier, Make Connecting and orchestrating APIs across platforms (Notion, Sheets, Gmail, etc.) JSON scripting and light JavaScript for data manipulation in tools Using Whisper, ElevenLabs, CapCut, Canva APIs for voice/video automation etc. CRM and outreach tooling: Go High Level, HubSpot, Clearbit, Al-generated content pipelines (blogs, carousels, videos, voiceovers)
An AI Solutions Architect is responsible for designing and deploying AI-powe'red systems and applications. They work with businesses to create scalable AI infrastructures that meet enterprise needs. Job Responsibilities Design AI system architectures for scalable solutions Work closely with development teams to implement AI models Ensure AI systems are secure and comply with best practices Evaluate and select AI tools and frameworks Oversee deployment of AI solutions in cloud or on-premise Collaborate with business stakeholders to ensure AI system meets business goals Required Skills Deep understanding of AI/ML frameworks and architectures Proficiency with cloud platforms (AWS, Google Cloud, Azure) Experience with AI model deployment and scaling Knowledge of security best practices in AI implementations Familiarity with API integration and microservices architecture Strong communication and project management skills
A Machine Learning Engineer designs and develops machine learning models to automate processes and improve decision-making. They work on a variety of algorithms, deploying models, and optimizing them for performance. Job Responsibilities Design and develop machine learning models Select appropriate algorithms for given tasks Optimize and tune models for better performance Collaborate with data scientists to prepare data for modeling Deploy models into production environments Stay updated with latest advancements in ML algorithms and tools Required Skills Proficiency in programming languages like Python, Java, or C++ Experience with ML frameworks like TensorFlow or PyTorch Strong understanding of algorithms and data structures Familiarity with cloud services (eg, AWS, Google Cloud) Knowledge of model optimization techniques Ability to work with large datasets and scalable systems