AI Lead Engineer

AI Lead EngineerExperience : 5+ Years | Type : Full-Time | Location : WFHRequirements : -Minimum of 5+ years of experience in AI / ML engineering, data science, or algorithm development. Strong experience in machine learning, deep learning, NLP, or computer vision.-Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, and scikit-learn.-Experience with cloud platforms (AWS / GCP / Azure) and MLOps tools (Docker, Kubernetes, MLflow).-Solid understanding of algorithms, data structures, statistics, and optimization techniques.-Excellent written and verbal communication skills.-Strong problem-solving, analytical, and leadership capabilities.-Degree in Computer Science, Data Science, AI / ML, Engineering, or a related field.Preferred (GenAI & LLM Experience) : -Experience with Generative AI and Large Language Models (LLMs), including fine-tuning, evaluation, and deployment.-Knowledge of prompt engineering and agentic workflows.-Familiarity with GenAI frameworks such as LangChain, LangGraph, LlamaIndex, Langfuse, CrewAI.-Understanding of RAG pipelines and vector databases such as Pinecone, FAISS, Chroma, OpenSearch.-Exposure to model optimization techniques like quantization (INT4 / GPTQ) and inference acceleration.Responsibilities : -Familiarize yourself with all AI / ML products, tools, and platforms used within the company.-Lead the design and development of scalable algorithms and machine learning models.-Develop and execute technical strategies and roadmaps aligned with business goals.-Build, train, test, and optimize ML and deep learning models for various use cases.-Identify, qualify, and propose AI / ML solutions to solve business challenges.-Collaborate with product managers, engineers, and stakeholders for requirement gathering and solution delivery.-Conduct research on emerging AI techniques and integrate relevant advancements.-Oversee deployment, monitoring, and performance tuning of models in production.-Provide mentorship to junior engineers and enforce technical best practices.-Maintain documentation related to models, datasets, architectures, and workflow processes.-Provide regular progress updates to the leadership team.