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AI Lead Engineer
1 month ago
AI Lead Engineer
Experience: 5+ Years | Type: Full-Time | Location: WFH
Requirements:
-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.
