A career in IBM Consulting is rooted by long-term relationships and close collaboration with clients across the globe.You'll work with visionaries across multiple industries to improve the hybrid cloud and AI journey for the most innovative and valuable companies in the world. Your ability to accelerate impact and make meaningful change for your clients is enabled by our strategic partner ecosystem and our robust technology platforms across the IBM portfolio including Software and Red Hat.Curiosity and a constant quest for knowledge serve as the foundation to success in IBM Consulting. In your role you'll be encouraged to challenge the norm investigate ideas outside of your role and come up with creative solutions resulting in groundbreaking impact for a wide network of clients. Our culture of evolution and empathy centers on long-term career growth and development opportunities in an environment
Role Overview
We are looking for a Senior/Lead ML Data Scientist with strong expertise in the Databricks ML ecosystem and proven experience in Generative AI and LLM fine-tuning. This role will drive end-to-end ML/AI initiatives — from presales solution shaping customer workshops and PoCs to large-scale delivery deployment and adoption. The candidate will define AI/ML strategy ensure successful execution and mentor teams while driving responsible and business-aligned AI delivery.
Key Responsibilities
ML & AI Solutioning
- Lead the design and development of machine learning models (classification regression clustering NLP CV).
- Implement ML workflows in Databricks using MLflow Feature Store AutoML and Databricks notebooks.
- Optimize and scale training using distributed ML frameworks (Spark MLlib Horovod Databricks Runtime for ML).
Presales & Client Engagement
- Partner with sales and consulting teams to support presales activities including solution design RFP responses and client presentations.
- Conduct workshops PoCs and live demos showcasing Databricks ML and GenAI capabilities.
- Translate complex ML/AI solutions into business value for CXOs and client stakeholders.
- Create thought leadership material (whitepapers PoVs reference architectures) to drive market presence.
Delivery & Execution
- Own the end-to-end execution of ML/GenAI projects — from requirements gathering to production deployment.
- Ensure scalable secure and cost-optimized delivery on Databricks and cloud ML platforms.
- Collaborate with cross-functional teams (data engineering application engineering cloud infra) to deliver high-quality outcomes.
- Establish success metrics monitor delivery performance and ensure client satisfaction.
GenAI / LLM Workloads
- Fine-tune and optimize LLMs (OpenAI Llama Falcon MPT HuggingFace Transformers) for domain-specific use cases.
- Implement Retrieval Augmented Generation (RAG) pipelines for enterprise search chatbots and knowledge assistants.
- Evaluate deploy and monitor custom fine-tuned models within Databricks Model Serving or cloud ML platforms.
- Collaborate with engineering teams to integrate GenAI capabilities into business applications.
MLOps & Governance
- Establish MLOps best practices with Databricks MLflow (experiment tracking model registry deployment pipelines).
- Implement automated CI/CD for ML pipelines with GitHub Actions Azure DevOps or Jenkins.
- Define and enforce Responsible AI practices: fairness explainability (SHAP LIME) bias detection compliance.
Leadership & Collaboration
- Mentor and guide junior data scientists and engineers.
- Partner with business leaders to identify AI opportunities and define strategy.
- Advocate for data-driven decision making across the organization.
Mandatory Skills
- Strong experience in Databricks ML ecosystem :
- MLflow (tracking registry deployment).
- Feature Store for feature management.
- AutoML for model experimentation.
- Databricks notebooks & pipelines.
- Proven expertise in LLM fine-tuning prompt engineering embeddings and RAG pipelines .
- Strong foundation in ML & DL frameworks (Scikit-learn TensorFlow PyTorch).
- Hands-on with Python Spark SQL for data science workflows.
- Proficiency with cloud ML platforms (Azure ML AWS SageMaker GCP Vertex AI).
- Experience with large-scale model training optimization and deployment .
- Strong customer-facing presales experience and delivery ownership in AI/ML projects.
Good to Have
- Familiarity with Databricks MosaicML for efficient LLM fine-tuning.
- Hands-on with vector databases (Pinecone Weaviate Milvus FAISS) for RAG.
- Exposure to streaming ML inference (Kafka Event Hub Kinesis).
- Certifications: Databricks ML Specialist Databricks Generative AI Associate Azure AI Engineer AWS ML Specialty.