Senior DevOps Engineer

At Innover, we endeavor to see our clients become connected, insight-driven businesses. Our integrated Digital Experiences, Data & Insights and Digital Operations studios help clients embrace digital transformation and drive unique outstanding experiences that apply to the entire customer lifecycle. Our connected studios work in tandem to reimagine the convergence of innovation, technology, people, and business agility to deliver impressive returns on investments. We help organizations capitalize on current trends and game-changing technologies molding them into future-ready enterprises.

Take a look at how each of our studios represents deep pockets of expertise and delivers on the promise of data-driven, connected enterprises.

About the Role:

Seeking a Senior Data Scientist who is equally comfortable building predictive models and architecting intelligent systems with Generative AI. This role is ideal for someone who thrives on solving complex business problems with the power of data, machine learning, and cutting-edge AI techniques — including large language models (LLMs), RAG pipelines, and prompt engineering.

You’ll be at the forefront of innovation, driving the design and deployment of scalable AI solutions that power intelligent products, enhance automation, and enable smarter decision-making across the organization.

Generative AI & LLMs -

• Fine-tune and apply large language models (LLMs) for summarization, classification, generation, and dialogue use cases.
  
• Design and implement retrieval-augmented generation (RAG) pipelines using vector databases.
  
• Develop intelligent agents, agentic workflows and chat-based interfaces that translate natural language into actions or insights.
  
• Build multi-agent orchestration frameworks using tools such as LangGraph, AutoGen, or similar for enterprise-grade use cases.
  
• Create reusable prompt templates and evaluation strategies to ensure output quality and relevance.

Machine Learning & Predictive Analytics -

• Develop robust ML models for classification, regression, clustering, and time-series forecasting.
  
• Apply advanced feature engineering, model evaluation, and explainability techniques.
  
• Collaborate with stakeholders to define data-driven solutions aligned with business KPIs.

Deployment & MLOps -

• Lead the full model lifecycle from experimentation to deployment preferably using Azure, Databricks, or equivalent platforms.
  
• Monitor model performance, automate retraining, and ensure reliability through MLOps best practices.

Data Strategy & Collaboration -

• Work with data engineers to transform raw data into ML-ready datasets.
  
• Collaborate cross-functionally with product managers, developers, and business teams to turn use cases into scalable AI solutions.
  
• Mentor junior team members and contribute to AI/ML best practices across the org.
  

• 4+ years of experience in data science or machine learning roles.
  
• Bachelor's, Master's degree, or Ph.D. in Computer Science, Data Science, Statistics, or a related field.
  
• Strong machine learning skills and 3+ years of experience productionizing machine learning models (Sklearn, XGBoost, or Deep Learning)
  
• Hands-on experience with Gen AI-related tech stack, including Azure cognitive search, Langchain, HuggingFace, vector DBs and agent frameworks like Autogen.
  
• Experience in fine-tuning open-source models like Llama etc. is a bonus.
  
• Strong programming skills in advanced python, with experience in SQL and cloud-based data platforms (Azure preferred).
  
• Understanding of model governance, versioning, monitoring, and evaluation.
  
• Ability to translate business problems into analytical frameworks and actionable models.
  
• Strong problem-solving mindset and ability to work in ambiguous, fast-paced environments.
  
• Strong storytelling and stakeholder communication skills.