AI Lead Engineer

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.