Ai Consultant Jobs

About The Position

Requirements

• 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 or proof-of-concept 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.

Nice To Haves

• 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

Responsibilities

• Work directly with customers and business stakeholders to understand complex use cases and translate them into AI and Generative AI solutions.
• Design, develop, and deploy production-grade applications using LLMs and machine learning technologies.
• Build 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.
• Integrate AI capabilities with enterprise applications, APIs, databases, cloud platforms, and business workflows.
• Perform prompt engineering and optimization, model evaluation, and guardrail implementation.
• Develop backend services and APIs using Python, FastAPI, or similar frameworks.
• Implement vector-search solutions using 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 LLMOps/MLOps practices for deployment, monitoring, evaluation, observability, versioning, and continuous improvement.
• Develop proof-of-concepts and 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.