AI Lead Engineer

4 weeks ago

Bikaner, India TekGenio Full time

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.

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