Gen AI Architect

Responsibilities

• Be an expert source on machine learning to drive delivery of new and innovative solutions.
• Propose creative solutions to approach business solutions with emerging technologies.
• Prototype new ways of applying technologies for solving business problems.
• Educate others so that they can demonstrate the innovative methods for achieving outcomes.
• Build and maintain machine learning principles, best practices, and code accelerators.
• Conduct external research and internal experimentation for machine learning techniques.
• Champion solution delivery behaviors and approaches from software engineers that accelerate delivery of reliable solutions and create a culture of teamwork.
• Analyze and communicate strategy, status, and product roadmaps to multiple audiences, including all levels of management.

• GenAI Application Development Expertise
• Programming
  
  Languages:
  
  Python
• Development Tools:
  Lang Chain, Llama Index, Lang Flow, Langgraph, Lang Smith, Flowise
• Additional Technologies:
  Knowledge Graphs, FastAPI, Streamlit, Gradio
• Languages & Libraries:
  Python, Data Engineering, OSS LLMs (Llama2, Mixtral, GPT-Neo, GPT-J)
• Tokenization & Frameworks:
  Tokenizers (Sentence Piece, Hugging Face Tokenizers), Fine-tuning Frameworks (Hugging Face Transformers, PyTorch Lightning)
• Datasets:
  Hugging Face Datasets, Tensor Flow Datasets
• Monitoring & Management:
  Ray, Seldon Core, MLFlow, MLServer, Triton, Bento
  
  ML, Prometheus, Grafana
• Workflow Automation:
  Apache Airflow, dbt, Apache NiFi, Fivetran, Airbyte, Great Expectations
• Threat Modeling & Security: AI-Specific Threat Modeling Tools, Secure ML Pipeline Tools, API Security Tools
• Monitoring & Prevention: AI Security Monitoring Tools, Prompt Injection Prevention Libraries, Adversarial Example Detection Libraries
• Ethics & Fairness: AI Ethics Frameworks and Tools, Bias Detection and Mitigation Tools, Fairness Metrics Libraries
• Privacy & Security:
  Privacy-Preserving Machine Learning Libraries, Robustness and Security Tools
• Model Interpretability & Governance:
  Model Interpretability Libraries, AI Governance Frameworks and Tools
• Infrastructure & CI/CD:
  Dev Ops, Kubernetes, Docker, Git, Jenkins, Git Lab, Git Hub Actions
• Data Engineering:
  Python, Apache Spark, Apache Kafka, AWS S3, Azure Data Lake Storage (ADLS), Delta Lake
• Data Catalogs:
  Amundsen, Collibra, Alation

• Bachelor’s degree in computer science, Data Science, or a related field; master’s degree preferred.
• 5+ years of professional and/or postgraduate academic research experience in software engineering.
• Preferred experience in SAP / Salesforce or Oracle programs.
• 4+ years of experience designing and developing machine learning solutions.
• 3+ years of experience with cloud native engineering, AWS, Azure, Google.

• Lead a team of junior developers.
• Ability to translate requirements into understandable technical design.
• Task effort estimation and distribution among team members.
• Excellent communication skills and stakeholder management.
• Ability to work with cross functional teams and business users.

Base Salary Range : $160,000 to $200,000 Per Annum

• Discretionary Annual Incentive.
• Comprehensive Medical Coverage:
  Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
• Family Support:
  Maternal & Parental Leaves.
• Insurance Options:
  Auto & Home Insurance, Identity Theft Protection.
• Convenience & Professional Growth:
  Commuter Benefits & Certification & Training Reimbursement.
• Time Off:
  Vacation, Time Off, Sick Leave & Holidays.
• Legal & Financial Assistance:
  Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.