Data Scientist

We are seeking an experienced Data Scientist (4-7 years) to build and optimize AI models that power our next-generation agentic AI systems. You will work on designing, deploying, and scaling AI-driven solutions that adapt, learn, and operate autonomously. The ideal candidate is adept at handling large-scale data, developing ML pipelines, and provisioning AI models for self-hosted and cloud-based environments.

Responsibilities:

• Develop and optimize machine learning models for agentic AI applications, including autonomous decision-making, reasoning, and planning.
• Design and implement scalable ML pipelines for real-time and batch inference, supporting high-performance AI workloads.
• Work with MLOps and engineering teams to deploy self-hosted AI models efficiently, ensuring optimized inference and minimal latency.
• Utilize distributed training techniques (e. g., Horovod, DeepSpeed) to enhance model scalability across multi-GPU or multi-node environments.
• Implement model fine-tuning, prompt engineering, and continuous learning systems to improve AI adaptability.
• Conduct rigorous model evaluation, including accuracy, fairness, and performance benchmarking.
• Stay updated with advancements in LLMs, reinforcement learning, and deep learning techniques relevant to agentic AI.

Requirements:

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field.
• 4-7 years of experience in applied machine learning, AI model development, or data science roles.
• Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or JAX.
• Experience in developing, training, and deploying LLMs and transformer-based models.
• Strong knowledge of distributed computing frameworks for AI scalability (e. g., Kubernetes, Ray, Horovod, DeepSpeed).
• Hands-on experience in model optimization, quantization, and inference acceleration.
• Expertise in self-hosted AI model provisioning, cloud/on-premise AI infrastructure, and containerization (Docker, Kubernetes).
• Familiarity with reinforcement learning, autonomous systems, or decision intelligence is a plus.

Preferred Skills:

• Experience with retrieval-augmented generation (RAG), fine-tuning, and knowledge distillation for AI models.
• Strong understanding of prompt engineering, embeddings, and vector search (FAISS, Pinecone, Weaviate).
• Background in working with large-scale datasets, including data pipelines, feature engineering, and model monitoring.
• Familiarity with AI observability and monitoring frameworks for model performance tracking.