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As a Mid-Level AI Engineer at Dogma Group, you will work in a client services environment delivering AI solutions under real deadlines and changing requirements. This is a hands-on role centered on building agentic systems backed by LLMs and computer vision applications, then deploying them as containerized, production-ready services. You will work with multi-agent architectures, modern retrieval pipelines, and standardized agent protocols, translating client requirements into working solutions within compressed timelines.

Design, build, and deploy single-agent and multi-agent systems backed by LLMs, using LangChain and LangGraph where appropriate, or built from scratch in Python with Pydantic for structured outputs and data validation Connect agents to tools, data sources, and other agents using standardized protocols such as MCP (Model Context Protocol) and A2A (Agent-to-Agent) Build production-grade RAG pipelines using hybrid retrieval (dense vector search combined with keyword search) and reranking with cross-encoders to improve answer grounding and reduce hallucination Work with vector databases such as Pinecone, Weaviate, Qdrant, or pgvector, selecting the right retrieval architecture for each client's scale and data requirements Build and integrate computer vision solutions including object detection, image classification, segmentation, and OCR, using OpenCV, TensorFlow or PyTorch, and vision-language models for multi-modal understanding Containerize AI services using Docker and manage deployments across cloud and on-premise environments Deploy and maintain models and agentic services in production, with observability, tracing, and evaluation pipelines to monitor performance, drift, and reliability Translate client requirements into technical scope and deliver iteratively under tight timelines Build data pipelines for training and inference, ensuring clean and reliable data flow Write maintainable Python code with proper documentation, testing, and version control practices Communicate progress and technical tradeoffs clearly to both technical and non-technical stakeholders Participate in client calls, demos, and reviews as a technical representative of the team Evaluate new AI tools, models, protocols, and frameworks for adoption across client projects