Senior Data scientist

Sigmoid Analytics seeks a Senior Data Scientist with expertise in Generative AI to join our dynamic team. This role is crucial for architecting, developing, and deploying cutting-edge machine learning models and AI solutions into production environments. You will collaborate closely with data scientists to transform prototypes into scalable, enterprise-ready applications, focusing on leveraging LLMs, retrieval-augmented generation (RAG), knowledge graphs, and multi-agent orchestration to design and implement enterprise AI systems.

Key responsibilities include seamlessly embedding AI models into existing products, APIs, and enterprise systems, conducting rigorous experimentation, fine-tuning, and error analysis to enhance model accuracy and reliability. You will also be responsible for continuously monitoring, maintaining, and retraining deployed models, staying abreast of the latest advancements in AI/ML, MLOps, and cloud-native AI technologies. A significant part of this role involves collaborating with stakeholders to translate business challenges into effective AI-driven solutions while upholding ethical standards and responsible AI principles.

Desired Skills and Competencies:

• Strong learning acumen and a high sense of ownership.
• Ability to thrive in a fast-paced, deadline-driven environment.
• Passion for technology and exceptional data interpretation skills.
• Proven problem-solving abilities.
• Expert programming skills in Python, including frameworks like TensorFlow, PyTorch, and scikit-learn.
• Proficiency with GenAI frameworks (e.g., LangGraph, AutoGen, OpenAI SDK) and prompt engineering on foundation models (e.g., OpenAI, HuggingFace, Anthropic).
• Knowledge of NLP, computer vision, or generative AI concepts, including LLMs, diffusion models, RAG, model fine-tuning, conversational AI, and knowledge graphs.
• Familiarity with cloud AI services such as AWS SageMaker, Azure ML, and GCP Vertex AI.
• Solid understanding of data structures, algorithms, and software engineering principles.
• Hands-on experience with model deployment using APIs, microservices, and real-time inference.

Nice-to-Have Qualifications:

• Experience with MLOps tools like MLflow, Kubeflow, Airflow, Docker, and Kubernetes.
• Experience with big data technologies such as Spark, Hadoop, and Databricks.
• Exposure to responsible AI frameworks and bias detection methods.
• Contributions to open-source AI/ML projects.

Experience and Education Requirements:

• 2 to 3.5 years of relevant Machine Learning experience.
• A B.Tech degree from a Tier-1 college or an M.S./M.Tech degree in Computer Science, Information Technology, or a related field is preferred.