Overview

We are looking for a highly skilled Lead AI/ML Engineer with 6 years of hands-on experience in designing, deploying, and scaling AI/ML and Generative AI solutions.

Responsibilities

• Drive the technical vision and roadmap for AI/ML and Generative AI initiatives.
• Lead and mentor a team of AI/ML engineers and data scientists, conducting regular code reviews and guiding solution architecture.
• Collaborate with cross-functional stakeholders to translate business problems into scalable AI/ML solutions.
• Own the AI/ML lifecycle from prototyping and experimentation to production deployment and monitoring.
• Architect and lead the development of scalable Generative AI solutions using AWS Cloud and modern frameworks.
• Deep experience in working with large language models (LLMs), including fine-tuning, evaluation, and deployment.
• Expertise in prompt engineering, agentic workflows, and custom LLM development.
• Proficient with LangChain, Langgraph, Langfuse, Crew AI, and other GenAI frameworks.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines for enterprise applications.
• Solve complex machine learning problems using supervised, unsupervised, and deep learning methods.
• Lead the implementation of ML pipelines including automated data preprocessing, training, validation, and monitoring.
• Manage ML lifecycle using AWS SageMaker, including model versioning, drift detection, and retraining strategies.
• Apply advanced analytics, time series forecasting, and predictive modeling techniques.
• Design and deploy advanced computer vision models for tasks such as classification, object detection, segmentation, and image captioning.
• 6 years of experience in AI/ML, with at least 3 years in Generative AI and LLMs.
• Strong proficiency in Python and ML libraries such as Scikit-learn, XGBoost, LightGBM, CatBoost.
• Deep understanding of LLMs including OpenAI (GPT-4, GPT-4o), Claude, Gemini, LLaMa, DeepSeek, etc.
• Proficiency with GenAI tooling: Langchain, Langfuse, Crew AI, LLAMA Index, Langgraph, etc.

Qualifications

• Hands-on with deep learning frameworks such as PyTorch and TensorFlow.
• Strong experience with AWS services: SageMaker, Bedrock, DynamoDB, S3, Lambda, etc.
• Proficiency in REST API development using FastAPI, Uvicorn, Flask, Docker, and related tools.
• Strong database knowledge—both SQL (PostgreSQL) and NoSQL (DynamoDB).
• Familiarity with caching systems like Redis and Memcached.
• Strong communication skills and ability to lead discussions with both technical and non-technical stakeholders.
• Contributions to open-source GenAI/ML projects.
• Certifications in AWS AI/ML or equivalent.