AI ML Lead - Immediate Joiner

We are seeking an experiencedAI/ML Leadwith strong expertise inTraditional Machine Learning, Deep Learning, Generative AI (LLMs), and Harness Engineering . The selected candidate will be responsible for designing, developing, and scaling robust AI solutions, while contributing to architectural decisions and providing technical leadership.Key ResponsibilitiesDesign and implementend-to-end AI/ML and Generative AI solutionsDevelop and optimizeRAG pipelines, LLM-based applications, and Agentic AI systemsOwn thecomplete ML lifecycle , including development, evaluation, deployment, monitoring, drift detection, and retrainingBuild and maintainML pipelines, MLOps frameworks, and Harness Engineering practicesfor experimentation and benchmarkingEstablishevaluation frameworks, observability, and performance monitoringfor ML and GenAI systemsCollaborate with cross-functional teams acrossengineering, product, and business unitsContribute topre-sales activities , including solution architecture, proposals, and effort estimationProvide technical leadership throughmentoring, code reviews, and best practice implementationRequired Skills & ExpertiseStrong proficiency inPython and applied StatisticsExpertise inTraditional Machine Learning , including model selection, feature engineering, and evaluation metrics (Precision, Recall, AUC)Solid understanding ofDeep Learning architectures , including TransformersHands‑on experience withPyTorch, TensorFlow, and scikit‑learnProven experience withLarge Language Models (LLMs)(public and private)Hands‑on experience in buildingRAG‑based applicationsStrong understanding ofembeddings, chunking, and prompt engineeringExperience inLLM evaluation, hallucination mitigation, and observabilityDemonstratedhands‑on experience in building Agentic AI systemsExperience building scalable Agentic AI applications in production environmentsWorking experience NL2SQLinAgentic AI solutionsExperience withfine‑tuning techniques (LoRA, QLoRA)Experience in developingevaluation harnesses for ML and LLM systemsStrong understanding of:Experiment tracking and reproducibilityAutomated benchmarking and testing frameworksModel and prompt versioning, and comparative evaluationExposure toend‑to‑end MLOps practices , including CI/CD for ML systemsExperience withLangChain, LangGraph, LlamaIndex, CrewAI, or similar frameworksWorking experience ofvector databasessuch as Pinecone, OpenSearch, or FAISSExperience withAWS AI services , including SageMaker and Bedrock (Guardrails, Agents, Knowledge Base)Familiarity withDocker, REST APIs, and CI/CD pipelines#J-18808-Ljbffr