Senior AI ML Engineer

We are seeking Senior AI/ML Engineers with 3 to 6 years of experience in implementing, deploying, and scaling AI/ML solutions. This role involves working with generative AI, machine learning, deep learning, and data science to solve business challenges by designing, building, and maintaining scalable and efficient AI/ML applications.

Key Responsibilities:

AI:

• Architect scalable Generative AI and Machine Learning applications using AWS Cloud OR GCP cloud and other cutting-edge technologies.
• Extensive experience with LLMs and various prompt engineering techniques.
• Fine-tune and build custom LLMs.
• Deep understanding of LLM architecture and internal mechanisms.
• Experience with Langchain, Langgraph, Langfuse, Crew AI, LLM output evaluations, and agentic workflows.
• Build RAG (Retrieval-Augmented Generation) pipelines and integrate them with traditional applications.

Data Science & Machine Learning:

• Solve complex data science problems and uncover insights using advanced EDA techniques.
• Implement automated pipelines for data cleaning, preprocessing, and model re-training.
• Hands-on experience with model experiment tracking and validation techniques.
• Deploy, track, and monitor models using AWS SageMaker.
• Strong knowledge of fundamental machine learning concepts, including supervised and unsupervised learning, deep learning, CNNs, and RNNs.
• Proficiency in working with databases for efficient data storage and retrieval.
• Experience with data warehouses and data lakes.

Computer Vision:

• Work on complex computer vision problems, including image classification, object detection, segmentation, and image captioning.

Skills & Qualifications:

• 2-3 years of experience in implementing, deploying, and scaling Generative AI solutions.
• 3-7 years of experience in NLP, Data Science, Machine Learning, and Computer Vision.
• Proficiency in Python and ML frameworks such as LangChain, Langfuse, LLAMA Index, LangGraph, CrewAI, and LLM output evaluations.
• Experience with AWS Bedrock, OpenAI GPT models (GPT-4, GPT-4o, GPT-4o-mini), and LLMs such as Claude, LLaMA, Gemini, and DeepSeek.
• Experience with vector databases like Pinecone, OpenSearch, FAISS, and Chroma, with a strong understanding of indexing mechanisms.
• Expertise in forecasting, time series analysis, and predictive analytics.
• Experience with classification, regression, clustering, and other ML models.
• Proficiency in SageMaker for model training, evaluation, and deployment.
• Hands-on experience with ML libraries such as Scikit-learn, XGBoost, LightGBM, and CatBoost.
• Experience with deep learning frameworks such as PyTorch and TensorFlow.
• Familiarity with Docker, Uvicorn, FastAPI, and Flask for REST APIs.
• Proficiency in SQL and NoSQL databases, including PostgreSQL and AWS DynamoDB.
• Experience with caching technologies such as Redis and Memcached.