Generative AI Engineer

As a Generative AI Engineer, you will be a senior-level engineer with 4-7 years of experience, specializing in designing, building, and deploying production-grade generative AI and agentic-AI solutions. Your primary responsibility will be to deliver secure, scalable, and business-oriented AI systems that operate on structured and unstructured data to enable AI-driven decision-making. Key Responsibilities: - Architect, develop, test, and deploy generative-AI solutions for domain-specific use cases using online/offline LLMs, SLMs, TLMs. - Design and implement agentic AI workflows and orchestration using frameworks like LangGraph, Crew AI, or equivalent. - Integrate enterprise knowledge bases and external data sources via vector databases and Retrieval-Augmented Generation (RAG). - Build and productionize ingestion, preprocessing, indexing, and retrieval pipelines for structured and unstructured data (text, tables, documents, images). - Implement fine-tuning, prompt engineering, evaluation metrics, A/B testing, and iterative model improvement cycles. - Conduct/model red-teaming and vulnerability assessments of LLMs and chat systems using tools like Garak. - Collaborate with MLOps/platform teams to containerize, monitor, version, and scale models ensuring model safety, bias mitigation, access controls, and data privacy compliance. - Translate business requirements into technical designs with clear performance, cost, and safety constraints. Required Skills and Experience: - Strong proficiency in Python and experience with ML/AI libraries such as scikit-learn, TensorFlow, PyTorch, Hugging Face ecosystem. - Hands-on experience with LLMs, RAG, vector databases, and retrieval pipelines. - Practical experience deploying agentic workflows and building multi-step, tool-enabled agents. - Experience using Garak (or similar LLM red-teaming/vulnerability scanners) to identify model weaknesses and harden deployments. - Demonstrated experience implementing content filtering/moderation systems. - Familiarity with cloud GenAI platforms and services (Azure AI Services preferred; AWS/GCP acceptable). - Experience in building APIs/microservices; containerization (Docker), orchestration (Kubernetes). - Strong understanding of model evaluation, performance profiling, inference cost optimization, and observability. - Good knowledge of security, data governance, and privacy best practices for AI systems. Preferred Qualifications: - Hands-on fine-tuning experience and parameter-efficient tuning methods. - Experience with multimodal models and retrieval-augmented multimodal pipelines. - Prior work on agentic safety, LLM application firewalls, or human-in-the-loop systems. - Familiarity with LangChain, LangGraph, Crew AI, or similar orchestration libraries. In this role, you are expected to have an AI-first thinking, a data-driven mindset, collaboration & agility, problem-solving orientation, business impact focus, and continuous learning approach.,