Senior Data Scientist

Role & responsibilities

Sigmoid Analytics is a leading Data solutions company backed by Sequoia Capital. We are best in- end-to-end data value chain spanning across Data Science, Data Engineering and Data Ops. With data and technology at the core of our solutions, we are solving some of the toughest problems out there. Our culture is modelled around expertise and mutual respect with a team first mindset. You will work with teams that push the boundaries of what-is-possible and build solutions that energize and inspire.

Offices: New York | Dallas | San Francisco | Lima | Bengaluru The below role is for our Bengaluru office.

About the Role: You will work on a broad range of cutting-edge data science and machine learning problems across a variety of industries. You will be engaging with clients to understand their business context. If you are passionate to work on complex unstructured business problems that can be solved using data science and machine learning, we would like to talk to you.

Role: SDS Mandate Skills & Competencies: - Work on the end-to-end design and deployment of scalable AI and machine learning solutions in production environments.

• Develop and optimize large language model (LLM) applications using frameworks such as LangChain, LlamaIndex, and Haystack. - Drive Retrieval-Augmented Generation (RAG) pipelines with vector databases like Pinecone, FAISS, Weaviate, and Milvus. - Collaborate closely with other data scientists to transition models smoothly from research to production. - Build and manage comprehensive MLOps pipelines encompassing training, testing, deployment, monitoring, and retraining. - Architect AI solutions integrated into enterprise applications via APIs and microservices. - Stay updated on AI and Generative AI research, continually assessing emerging frameworks for adoption. - Mentor junior AI engineers and promote a culture of best practices in AI engineering. - Work with business stakeholders to translate requirements into AI-driven outcomes. - Ensure adherence to responsible AI principles, including bias mitigation, fairness, and explainability.

Must-Have: - 2.5- 3.5 years of experience in AI/ML engineering, with at least 1.5 years in leading AI initiatives. - Strong expertise in Python, TensorFlow, PyTorch, Hugging Face Transformers. - Hands-on experience with LangChain or similar LLM application frameworks. - Solid understanding of vector databases, RAG architectures, and prompt engineering. - Proficiency in MLOps tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes).

• Familiarity with cloud AI platforms (AWS SageMaker, GCP Vertex AI, Azure ML). - Strong background in data pipelines, APIs, and scalable system design.

Nice-to-Have: - Experience fine-tuning LLMs (LoRA, PEFT, parameter-efficient training). - Knowledge of computer vision or multimodal AI. - Familiarity with responsible AI frameworks and explainability tools (SHAP, LIME, Captum). - Contributions to open-source AI projects.

Desired Experience - 2.5-3. 5 years of relevant Machine Learning experience.