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 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, Crew AI, 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.