AI Engineer

Job Summary:

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.

Key Responsibilities:

• 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

Experience/Skills Required:

• 3+ years of relevant work experience in AI/ML development and deployment, with a Bachelor's Degree in Computer Science, Computer Engineering, Data Science, Machine Learning, or a related field
• Foundational knowledge of machine learning and deep learning concepts, including model training, evaluation, and optimization
• Mathematical knowledge in statistics, probability, linear algebra, and calculus as applied to ML and DL
• Strong knowledge of GenAI and LLMs, with hands-on experience building and integrating LLM-based applications
• Hands-on experience developing agentic systems in Python, building agents from scratch using Pydantic for data validation
• Familiarity with agentic frameworks such as LangChain and LangGraph, and standardized agent protocols such as MCP and A2A
• Experience with vector databases such as Pinecone, Weaviate, or Qdrant,, and an understanding of hybrid search and reranking for retrieval-augmented generation
• Proficient in TensorFlow and PyTorch, with a track record of taking models from development through deployment
• Experience with computer vision libraries and tools including OpenCV, YOLO, PIL, and deep learning frameworks for image processing tasks
• Hands-on experience containerizing and deploying AI services using Docker, with working knowledge of orchestration tools.
• Working knowledge of at least one major cloud platform (Azure preferred) for AI/ML deployment
• Strong analytical thinking, problem-solving ability, and clear communication for both technical and non-technical stakeholders
• Comfort working in a fast-paced, client-facing environment with shifting priorities and short delivery cycles.
• Awareness of privacy, data security, and ethical considerations in GenAI development, including responsible handling of client data and compliance with relevant regulations.