AI Lead Engineer

4 weeks ago

Kota, India TekGenio Full time

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.

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