Lead AI/ML Engineer
Primary Skills
Dialogue Design, Syntax and semantic analysis, Reinforcement learning, Text Classification, Governance for Conversational AI, RASA, Natural Language Understanding (NLU), Python, Kore.ai, GDF (Google Dialog Flow), Experience Cms Connect, IBM Watson, Automatic Speech Recognition (ASR), Dialogue Management (NLG), Spark NLP, Recurrent Neural Networks, Conv AI Testing
Specialization
Conversational AI: Lead AI/ML Engineer
Job requirements
Experience Range: 4 to 6 years of experience, including hands-on work in conversational AI and at least 2 years specifically in content management systems within media or content-driven environments Key Responsibilities: 1. Partner directly with product owners and content operations leads to shape ambiguous business ideas into scoped, testable AI requirements for content and media workflows 2. Rapidly prototype and deliver working proofs-of-concept for content use cases such as drafting, summarization, rewriting, taxonomy mapping, translation, and moderation, demonstrating solutions to stakeholders within days 3. Design and implement production architectures for LLM and agentic applications, including retrieval strategies, orchestration topologies, context and memory design, and human-in-the-loop checkpoints 4. Build and operate multi-agent systems using orchestration frameworks (e.g., LangGraph, CrewAI), ensuring robust tool/function calling, state management, and recovery from partial failures 5. Establish and enforce trust layers by implementing guardrails, input/output validation, PII/IP protection, and adversarial testing for secure and compliant AI deployments 6. Deploy and manage AI solutions on cloud platforms (Azure, AWS, GCP) with CI/CD, containerization, autoscaling, secrets management, and cost governance for token- and GPU-intensive workloads 7. Define and monitor quality metrics for content outputs, build evaluation datasets and harnesses, and instrument tracing, latency, and spend telemetry 8. Document architectural decisions, write runbooks, and upskill engineering teams to ensure system sustainability and knowledge transfer Required Skills: 1. Practical expertise with commercial LLMs (e.g., GPT, Claude, Gemini) and open-weight/small models (e.g., Llama, Mistral, Phi, Gemma) 2. Hands-on experience with content management systems (CMS) and integrating AI agents for content workflows 3. Strong proficiency in prompt engineering, few-shot design, retrieval augmented generation (RAG), and parameter-efficient fine-tuning (LoRA, QLoRA, PEFT) 4. Experience deploying SLMs via vLLM, Ollama, Triton, or managed equivalents 5. Advanced skills in vector store integration (Azure AI Search, OpenSearch, Pinecone, Weaviate, pgvector) 6. Programming expertise in Python for AI/ML development 7. Experience with containerization and CI/CD pipelines for cloud deployment (Azure, AWS, GCP) 8. Familiarity with orchestration frameworks for multi-agent systems (LangGraph, CrewAI) Preferred Skills: 1. Experience with adversarial testing and implementing security guardrails for AI systems 2. Knowledge of content enrichment, metadata management, and editorial workflow automation 3. Familiarity with evaluation harnesses and regression testing for prompt/model changes 4. Experience with asset localization, search and discovery, personalization, and content moderation in media environments Desired Qualifications: 1. Bachelor's degree in Computer Science, Information Technology, Artificial Intelligence, or a closely related field relevant to content/media AI 2. Certification in Conversational AI or NLP technologies (e.g., RASA Certified Developer, IBM Watson AI Certification) 3. Certification in cloud platforms (e.g., AWS Certified Machine Learning Specialty, Azure AI Engineer Associate, Google Professional Machine Learning Engineer)
