AI Engineering Consultant

About Us ABeam Consulting is a global professional services company that specializes in delivering business transformation and technology solutions to clients across a wide range of industries. With a global presence and over 9000 employees worldwide we aim to be the transformation partner of choice for all of our clients. Key Responsibilities • Work with business users and stakeholders to identify, assess, and prioritize potential AI and Agentic AI use cases. • Understand business processes, pain points, data availability, and system constraints, and translate them into appropriate AI solution designs. • Design, develop, test, and deploy enterprise-grade AI applications and AI agents. • Develop AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI frameworks. • Design and implement single-agent and multi-agent workflows, including agent communication, task delegation, decision logic, memory, and tool orchestration. • Develop orchestration workflows using technologies such as LangChain, LangGraph, MCP, and similar agent frameworks. • Integrate AI agents with enterprise applications, databases, APIs, document repositories, and external tools. • Develop robust backend services and APIs using Python and frameworks such as FastAPI. • Design and implement RAG pipelines covering document ingestion, chunking, embedding, retrieval, hybrid search, reranking, prompt construction, and response generation. • Build reusable AI components, agent tools, APIs, connectors, and services to accelerate future AI implementations. • Develop POCs rapidly and work with stakeholders to validate business feasibility, technical feasibility, and expected value. • Industrialize successful POCs into scalable and maintainable production solutions. • Implement appropriate security, access control, guardrails, governance, observability, and monitoring for enterprise AI applications. • Define evaluation frameworks and success metrics to assess AI agent performance, including response quality, accuracy, reliability, latency, token consumption, and business outcomes. • Establish feedback loops and continuously improve AI agent performance based on evaluation results and user feedback. • Troubleshoot and optimize AI applications, including prompts, retrieval quality, agent workflows, model selection, and system performance. • Collaborate with infrastructure, security, application, data, and architecture teams to support successful enterprise deployment. • Communicate AI concepts, solution architecture, limitations, risks, and recommendations clearly to both technical and non-technical stakeholders. • Prepare solution documentation, architecture designs, implementation plans, technical specifications, test scenarios, and operational documentation. • Provide knowledge transfer and technical guidance to client teams to support sustainable adoption of AI solutions. Key Requirements • Strong hands-on experience in AI Engineering, Generative AI, or Agentic AI development. • Practical experience designing and implementing solutions using LLMs. • Strong understanding of RAG architecture, including retrieval strategies, embeddings, vector search, hybrid search, reranking, and prompt engineering. • Hands-on experience with LangChain, LangGraph, or equivalent agent orchestration frameworks. • Experience designing multi-agent workflows and tool-calling/orchestration architectures. • Strong programming skills in Python. • Strong experience developing APIs, backend services, and integrations with enterprise applications. • Experience developing AI solutions from POC through production deployment. • Experience integrating AI solutions with structured and unstructured enterprise data sources. • Understanding of AI application architecture, security, governance, access controls, guardrails, observability, and monitoring. • Experience defining LLM/agent evaluation methodologies and performance metrics. • Familiarity with cloud platforms and managed AI services, preferably Azure / Azure OpenAI. • Experience and knowledge in Microsoft Copilot and Copilot Studio are mandatory; experience with Microsoft Foundry and Google Antigravity and Google Vertex AI are good to have. • Strong analytical and problem-solving capabilities. • Ability to work independently and deliver solutions in a fast-paced environment. • Strong communication, stakeholder management, and consulting skills. • Ability to work directly with business users to convert business requirements into practical AI solutions. Preferred / Added Advantage • Experience with MCP (Model Context Protocol) and agent tool integration. • Experience with self-hosted or open-source LLM deployment. • Experience with LLM evaluation frameworks such as DeepEval or equivalent. • Experience with model fine-tuning, including SFT or other post-training techniques. • Experience working with enterprise document-processing, OCR, knowledge-management, or search solutions. • Familiarity with Azure, AWS, containerization, and enterprise deployment architectures. • Experience working in consulting, banking, financial services, or other highly regulated enterprise environments. Other Requirements • Ability to work well within a multi-disciplinary team structure, but also independently. • Strong analytical and problem-solving skills across both rule-based and AI-driven scenarios. • Meet agreed deadlines, with demonstrable Productivity. • Strong interpersonal, verbal and written communication skills. • Ability to work in culturally diverse and inclusive environments. • Flexibility in scheduling with willingness to work extra non-standard hours if required. • Initiate, proactive and willingness to self-learn existing and new technology. • Willing and able to travel 50% or more (domestic, regional and international). Education Requirements • Degree in in Computer Science or related discipline. • Written and spoken…