AI ML Engineer - Lead

Key Responsibilities: Leadership & Strategy 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. Generative AI & LLMs 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.

Machine Learning & Data Science 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. Computer Vision Design and deploy advanced computer vision models for tasks such as classification, object detection, segmentation, and image captioning. Skills & Qualifications: 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.

Experience working with vector databases (Pinecone, FAISS, OpenSearch, Chroma) and indexing strategies. 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. Preferred: Experience in leading enterprise-scale AI/ML deployments. Contributions to open-source GenAI/ML projects.

Certifications in AWS AI/ML or equivalent.