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Lead Business Execution Consultant AI & Data Engineering Specialist

Lead Business Execution Consultant AI & Data Engineering Specialist

Wells Fargo

5 - 10 years

19 - 25 Lacs

bengaluru

Posted:-1 days ago| Platform:

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Skills Required

mlops kubeflow azure pytorch gcp mlflow aws tensorflow

Work Mode

Work from Office

Job Type

Full Time

Job Description

In this role, you will:

Lead cross functional teams to strategize, plan, and execute a variety of programs, services and initiatives
Drive accountability for assigned initiatives, limit risk exposure, and create efficiencies as appropriate
Review strategic approaches and effectiveness of support function and business performance
Perform assessments through fact finding and data requiring creative approaches to solving complex issues, and develop appropriate solutions or recommendations
Make decisions in highly complex and multifaceted situations requiring solid understanding of business group's functional area or products, facilitate decision making and issue resolution, and support implementation of developed solutions and plans
Collaborate and consult with members of the Business Execution team and team leaders to drive strategic initiatives
Influence, guide and lead less experienced Strategy and Execution staff within the group

Required Qualifications:

5+ years of Business Execution, Implementation, or Strategic Planning experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Desired Qualifications:

Bachelors or Masters degree in Computer Science, Data Engineering, AI/ML, or related field.
Hands on experience in AI/ML engineering, with hands-on model evaluation and deployment.
Strong programming skills in Python and familiarity with frameworks like TensorFlow, PyTorch, Hugging Face.
Experience with cloud platforms (AWS, GCP, Azure) and data pipeline tools (Spark, Kafka).
Proficiency in MLOps tools (MLflow, Kubeflow) and containerization (Docker, Kubernetes).

Preferred Experience:

Hands-on experience with Generative AI and LLM fine-tuning.
Knowledge of AI governance and ethical frameworks.
Familiarity with vector databases (e.g., Pinecone, Weaviate) and RAG pipelines.
Certifications in AI/ML engineering or cloud AI services.

Job Expectation:

Model Evaluation & Benchmarking:
- Assess AI models (LLMs, vision models, predictive ML) for accuracy, fairness, scalability, and cost-effectiveness.
- Design evaluation frameworks and metrics for performance comparison across different architectures.
Experimentation & Prototyping:
- Rapidly prototype new AI models and techniques, including fine-tuning and hyperparameter optimization.
- Implement Retrieval-Augmented Generation (RAG) and vector-based search for LLMs where applicable.
Data Engineering & Pipeline Development:
- Build and maintain scalable ETL pipelines to feed high-quality data into AI models.
- Ensure data integrity, preprocessing, and feature engineering for optimal model performance.
Deployment & MLOps:
- Deploy models into production using containerization (Docker) and orchestration tools.
- Utilize MLOps frameworks (e.g., MLflow, Kubeflow) for lifecycle management and monitoring.
Performance Monitoring & Continuous Improvement:
- Implement drift detection, bias monitoring, and automated retraining workflows.
- Stay current with emerging AI technologies and integrate best practices into evaluation processes.
Collaboration & Documentation:
- Work with data scientists, software engineers, and product teams to align AI solutions with business objectives.
- Maintain comprehensive documentation of evaluation methodologies, findings, and deployment strategies.

Required Skills:

Deep understanding of AI model architectures , including LLMs, transformers, and classical ML algorithms.
Ability to compare models across multiple dimensions (accuracy, latency, cost, interpretability).
Expertise in data engineering for AI workflows, including ETL and feature engineering.
Strong analytical and problem-solving skills with experience in statistical evaluation metrics .
Excellent communication skills for cross-functional collaboration.

Preferred Experience:

Hands-on experience with Generative AI and LLM fine-tuning .
Knowledge of AI governance and ethical frameworks .
Familiarity with vector databases (e.g., Pinecone, Weaviate) and RAG pipelines .
Certifications in AI/ML engineering or cloud AI services .

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