Data Scientist

4 weeks ago

Gurgaon, Haryana, India mool Full time

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.

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