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Data Scientist-Artificial Intelligence

Data Scientist-Artificial Intelligence

IBM

3 - 7 years

14 - 18 Lacs

mumbai

Posted:10 hours ago| Platform:

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Skills Required

tensorflow pytorch ml python pipeline scikit-learn dl kinesis azure devops sql data science spark gcp jenkins spark mllib github natural language processing event hub aws sagemaker machine learning data engineering data bricks kafka clustering aws

Work Mode

Work from Office

Job Type

Full Time

Job Description

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 practicesfairness, 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.

Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise 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.

Preferred technical and professional experience 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).

CertificationsDatabricks ML Specialist, Databricks Generative AI Associate, Azure AI Engineer, AWS ML Specialty.

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Data Scientist-Artificial Intelligence