We are seeking a highly skilled and passionate Data Scientist to design, build, and productionize enterprise-grade analytics and GenAI-powered solutions that enhance insights, recommendations, and decision-making across enterprise platforms.

The role focuses on developing, deploying, and operating machine learning and applied GenAI models—including LLM-based insight generation, summarization, and decision augmentation—using large-scale structured and semi-structured data, with strong emphasis on scalability, reliability, governance, and enterprise readiness.

Skills

Strong foundation in statistics, machine learning, and applied data science, including feature engineering, model evaluation, and performance tuning.

Experience building predictive, descriptive, and prescriptive models on large-scale structured and semi-structured data.

Proficiency in Python, SQL, and Spark, with hands-on experience in data processing and analytical pipelines.

Hands-on experience with the Databricks ecosystem (Databricks SQL, MLflow, Feature Store, and Jobs) to build, deploy, and monitor data science and GenAI solutions at enterprise scale.

Experience with ML frameworks such as PyTorch and/or TensorFlow for model development and experimentation.

Hands-on experience using LangChain and LangGraph to operationalize LLM-based analytical workflows, including RAG and prompt design, and evaluation techniques, with focus on analytical and decision-support use cases.

Practical exposure to MLOps / LLMOps practices, including model and prompt versioning, deployment, monitoring, and retraining.

Experience tracking model quality, drift, and GenAI output reliability in production.





Solid understanding of data quality, explainability, responsible AI, and enterprise governance requirements.

Qualifications and Experience

8+ years of experience in Data Science / AI Engineering, including:

6+ years building and deploying machine learning models (supervised, unsupervised, and time-series), covering feature engineering, model evaluation, and performance optimization.

4+ years working with NLP or language-based systems, including text classification, information extraction, and semantic modeling.

2+ years delivering GenAI or conversational AI solutions in production, with focus on applied LLM use cases, RAG, and enterprise deployment.

Roles and Responsibilities

Responsibilities

Advanced Analytics & Data Science

Translate business problems into data science, statistical, and machine learning solutions that drive measurable outcomes across enterprise use cases.

Perform data exploration, feature engineering, model development, and evaluation on large-scale structured and semi-structured datasets.

Build and deploy predictive, prescriptive, and descriptive models, ensuring interpretability, robustness, and alignment with business objectives.

Partner closely with business, product, and analytics teams to validate assumptions, define success metrics, and deliver actionable insights.





Applied GenAI & LLM Enablement

Apply GenAI techniques to augment data science workflows, including LLM-based insight generation, summarization, classification, and decision support.

Design and implement Retrieval-Augmented Generation (RAG) solutions to ground LLM outputs in enterprise data and analytical results.

Collaborate on GenAI-enabled analytical applications (e.g., conversational analytics, insight assistants) with a focus on accuracy, relevance, and explainability rather than pure agent orchestration.

Evaluate and benchmark GenAI outputs using quantitative and qualitative metrics, ensuring alignment with business and analytical standards.

Enterprise Productionization & MLOps / LLMOps

Productionize data science and GenAI models using enterprise-grade MLOps / LLMOps practices, including versioning, deployment, monitoring, and retraining strategies.

Build scalable, secure, and reliable analytical pipelines in collaboration with Data Engineering and Cloud teams.

Monitor model performance, data drift, and GenAI output quality, and drive continuous improvements based on real-world usage.

Ensure solutions meet enterprise requirements for governance, security, compliance, and responsible AI.

Performance Measurement & Continuous Improvement

Define and track model and GenAI performance metrics (accuracy, stability, bias, latency, business impact).

Run experiments and controlled rollouts to optimize models, GenAI prompts, and retrieval strategies.

Continuously enhance solutions through feedback loops, experimentation, and evolving business needs.

📌 Data Scientist (Pune)
🏢 Infovision labs
📍 Pune