Consultant - R01573317

Digital Engineering : Digital Engineering Employee-Hourly Consultant Primary Skills CSPO Specialization Product Management: Senior Software Development Lead Job requirements Job Title: AI Engineer – Forward Deployed Engineer (FDE) Job Summary We are looking for an experienced AI Engineer – Forward Deployed Engineer (FDE) to design, build, deploy, and operationalize AI solutions directly within customer environments. The role combines strong AI/ML and Generative AI engineering skills with hands-on customer engagement, solution architecture, and production deployment. The ideal candidate will work closely with customers, product teams, data engineers, and platform teams to translate business requirements into scalable AI solutions and accelerate adoption from prototype through production. Key Responsibilities Work directly with customers and business stakeholders to understand complex use cases and translate them into AI/GenAI solutions. Design, develop, and deploy production-grade applications using Large Language Models (LLMs) and machine learning technologies. Build Retrieval-Augmented Generation (RAG) pipelines using enterprise data sources, embeddings, vector databases, and semantic search. Develop AI agents and agentic workflows, including tool calling, orchestration, memory, and multi-step reasoning patterns. Integrate AI capabilities with enterprise applications, APIs, databases, cloud platforms, and existing business workflows. Perform prompt engineering, prompt optimization, model evaluation, and guardrail implementation. Develop backend services and APIs using Python, FastAPI, or similar frameworks. Work with LLM platforms and frameworks such as Azure OpenAI, OpenAI APIs, LangChain, LlamaIndex, Semantic Kernel, or equivalent technologies. Implement vector search solutions using technologies such as Azure AI Search, Pinecone, Weaviate, FAISS, or similar platforms. Design and implement scalable AI workloads on Azure, AWS, or Google Cloud Platform. Containerize and deploy AI applications using Docker and Kubernetes. Establish appropriate LLMOps/MLOps practices for deployment, monitoring, evaluation, observability, versioning, and continuous improvement. Develop proof-of-concepts and rapidly convert successful prototypes into production-ready solutions. Troubleshoot AI application issues across models, prompts, retrieval pipelines, APIs, infrastructure, and customer environments. Implement responsible AI practices covering security, privacy, access control, hallucination mitigation, content safety, and data governance. Collaborate with engineering and product teams to incorporate reusable customer solutions and feedback into the broader AI platform. Required Skills & Qualifications Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field. Strong hands-on programming experience with Python. Experience developing production applications using Generative AI and LLMs. Strong understanding of RAG, embeddings, vector databases, semantic search, prompt engineering, and agentic AI. Experience integrating AI applications with REST APIs, enterprise databases, and cloud services. Hands-on experience with at least one major cloud platform: Microsoft Azure, AWS, or GCP. Good understanding of software engineering principles, Git, CI/CD, testing, containerization, and API development. Knowledge of machine learning concepts, model evaluation, inference, and performance optimization. Experience taking AI solutions from prototype/PoC to production deployment. Strong troubleshooting and problem-solving skills. Excellent customer-facing communication and stakeholder-management abilities. Ability to work in fast-paced environments with ambiguous or evolving customer requirements. Preferred Skills Experience with Azure OpenAI Service, Azure AI Foundry, Azure AI Search, Amazon Bedrock, Google Vertex AI, or similar AI platforms. Experience building AI agents, copilots, conversational AI, or autonomous workflows. Knowledge of LLM evaluation techniques, observability, tracing, hallucination detection, and AI guardrails. Experience with PyTorch, Tensor